{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# 查看当前挂载的数据集目录, 该目录下的变更重启环境后会自动还原\n",
    "# View dataset directory. \n",
    "# This directory will be recovered automatically after resetting environment. \n",
    "!ls /home/aistudio/data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# 查看工作区文件, 该目录下的变更将会持久保存. 请及时清理不必要的文件, 避免加载过慢.\n",
    "# View personal work directory. \n",
    "# All changes under this directory will be kept even after reset. \n",
    "# Please clean unnecessary files in time to speed up environment loading. \n",
    "!ls /home/aistudio/work"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# 如果需要进行持久化安装, 需要使用持久化路径, 如下方代码示例:\n",
    "# If a persistence installation is required, \n",
    "# you need to use the persistence path as the following: \n",
    "!mkdir /home/aistudio/external-libraries\n",
    "!pip install beautifulsoup4 -t /home/aistudio/external-libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# 同时添加如下代码, 这样每次环境(kernel)启动的时候只要运行下方代码即可: \n",
    "# Also add the following code, \n",
    "# so that every time the environment (kernel) starts, \n",
    "# just run the following code: \n",
    "import sys \n",
    "sys.path.append('/home/aistudio/external-libraries')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#  实验1 百度在线编程平台基本使用\n",
    "## 实验目标\n",
    "* 熟悉平台环境使用\n",
    "* 编写第一个python\n",
    "## 实验环境"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-14T02:34:19.842909Z",
     "iopub.status.busy": "2022-03-14T02:34:19.842448Z",
     "iopub.status.idle": "2022-03-14T02:34:22.688860Z",
     "shell.execute_reply": "2022-03-14T02:34:22.687926Z",
     "shell.execute_reply.started": "2022-03-14T02:34:19.842871Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple\n",
      "Collecting rdflib\n",
      "  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/95/7f/7729b74154047bde5a6ac39a0c4d689b98808521417571bf5e92e2d3c2c2/rdflib-6.1.1-py3-none-any.whl (482 kB)\n",
      "     |████████████████████████████████| 482 kB 7.8 MB/s            \n",
      "\u001b[?25hRequirement already satisfied: importlib-metadata in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from rdflib) (4.2.0)\n",
      "Requirement already satisfied: pyparsing in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from rdflib) (3.0.7)\n",
      "Requirement already satisfied: setuptools in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from rdflib) (41.4.0)\n",
      "Collecting isodate\n",
      "  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/b6/85/7882d311924cbcfc70b1890780763e36ff0b140c7e51c110fc59a532f087/isodate-0.6.1-py2.py3-none-any.whl (41 kB)\n",
      "     |████████████████████████████████| 41 kB 159 kB/s             \n",
      "\u001b[?25hRequirement already satisfied: typing-extensions>=3.6.4 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from importlib-metadata->rdflib) (4.0.1)\n",
      "Requirement already satisfied: zipp>=0.5 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from importlib-metadata->rdflib) (3.7.0)\n",
      "Requirement already satisfied: six in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from isodate->rdflib) (1.16.0)\n",
      "Installing collected packages: isodate, rdflib\n",
      "Successfully installed isodate-0.6.1 rdflib-6.1.1\n",
      "\u001b[33mWARNING: You are using pip version 21.3.1; however, version 22.0.4 is available.\n",
      "You should consider upgrading via the '/opt/conda/envs/python35-paddle120-env/bin/python -m pip install --upgrade pip' command.\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "!pip install rdflib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-14T03:15:17.893570Z",
     "iopub.status.busy": "2022-03-14T03:15:17.893129Z",
     "iopub.status.idle": "2022-03-14T03:15:18.689246Z",
     "shell.execute_reply": "2022-03-14T03:15:18.688076Z",
     "shell.execute_reply.started": "2022-03-14T03:15:17.893537Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory: '小谭date/line_animation.cvs'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_132/2671317062.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mpandas\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;31m###获取中国gdp数据\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"小谭date/line_animation.cvs\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0myear\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'time'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36mread_csv\u001b[0;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, dialect, error_bad_lines, warn_bad_lines, delim_whitespace, low_memory, memory_map, float_precision)\u001b[0m\n\u001b[1;32m    686\u001b[0m     )\n\u001b[1;32m    687\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 688\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    689\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    690\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m_read\u001b[0;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[1;32m    452\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    453\u001b[0m     \u001b[0;31m# Create the parser.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 454\u001b[0;31m     \u001b[0mparser\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mTextFileReader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfp_or_buf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    455\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    456\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mchunksize\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0miterator\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[1;32m    946\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"has_index_names\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"has_index_names\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    947\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 948\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_engine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mengine\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    949\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    950\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m_make_engine\u001b[0;34m(self, engine)\u001b[0m\n\u001b[1;32m   1178\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_make_engine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mengine\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"c\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1179\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mengine\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"c\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1180\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mCParserWrapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1181\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1182\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mengine\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"python\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, src, **kwds)\u001b[0m\n\u001b[1;32m   2008\u001b[0m         \u001b[0mkwds\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"usecols\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0musecols\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2009\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2010\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_reader\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mparsers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTextReader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msrc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2011\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munnamed_cols\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_reader\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munnamed_cols\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2012\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader.__cinit__\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader._setup_parser_source\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: '小谭date/line_animation.cvs'"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "###获取中国gdp数据\n",
    "data=pd.read_csv(\"小谭date/line_animation.cvs\")\n",
    "\n",
    "year=data['time']\n",
    "china=data['china']\n",
    "usa=data['usa']\n",
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-21T05:33:26.426345Z",
     "iopub.status.busy": "2022-03-21T05:33:26.426027Z",
     "iopub.status.idle": "2022-03-21T05:33:27.566126Z",
     "shell.execute_reply": "2022-03-21T05:33:27.565405Z",
     "shell.execute_reply.started": "2022-03-21T05:33:26.426296Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>china</th>\n",
       "      <th>usa</th>\n",
       "      <th>king</th>\n",
       "      <th>japan</th>\n",
       "      <th>russia</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>241.000000</td>\n",
       "      <td>241.000000</td>\n",
       "      <td>241.000000</td>\n",
       "      <td>241.000000</td>\n",
       "      <td>241.000000</td>\n",
       "      <td>241.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1920.000000</td>\n",
       "      <td>3906.327801</td>\n",
       "      <td>19019.580913</td>\n",
       "      <td>14975.016598</td>\n",
       "      <td>11803.473029</td>\n",
       "      <td>9165.580913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>69.714896</td>\n",
       "      <td>7897.060024</td>\n",
       "      <td>20966.605026</td>\n",
       "      <td>14186.285966</td>\n",
       "      <td>15780.652526</td>\n",
       "      <td>10130.108008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1800.000000</td>\n",
       "      <td>560.000000</td>\n",
       "      <td>1965.000000</td>\n",
       "      <td>3043.000000</td>\n",
       "      <td>1009.000000</td>\n",
       "      <td>1365.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1860.000000</td>\n",
       "      <td>735.000000</td>\n",
       "      <td>3354.000000</td>\n",
       "      <td>4926.000000</td>\n",
       "      <td>1116.000000</td>\n",
       "      <td>1831.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1920.000000</td>\n",
       "      <td>785.000000</td>\n",
       "      <td>8134.000000</td>\n",
       "      <td>7973.000000</td>\n",
       "      <td>2620.000000</td>\n",
       "      <td>3415.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1980.000000</td>\n",
       "      <td>1078.000000</td>\n",
       "      <td>29523.000000</td>\n",
       "      <td>20733.000000</td>\n",
       "      <td>21614.000000</td>\n",
       "      <td>15604.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2040.000000</td>\n",
       "      <td>34823.000000</td>\n",
       "      <td>79957.000000</td>\n",
       "      <td>56528.000000</td>\n",
       "      <td>55158.000000</td>\n",
       "      <td>39276.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              time         china           usa          king         japan  \\\n",
       "count   241.000000    241.000000    241.000000    241.000000    241.000000   \n",
       "mean   1920.000000   3906.327801  19019.580913  14975.016598  11803.473029   \n",
       "std      69.714896   7897.060024  20966.605026  14186.285966  15780.652526   \n",
       "min    1800.000000    560.000000   1965.000000   3043.000000   1009.000000   \n",
       "25%    1860.000000    735.000000   3354.000000   4926.000000   1116.000000   \n",
       "50%    1920.000000    785.000000   8134.000000   7973.000000   2620.000000   \n",
       "75%    1980.000000   1078.000000  29523.000000  20733.000000  21614.000000   \n",
       "max    2040.000000  34823.000000  79957.000000  56528.000000  55158.000000   \n",
       "\n",
       "             russia  \n",
       "count    241.000000  \n",
       "mean    9165.580913  \n",
       "std    10130.108008  \n",
       "min     1365.000000  \n",
       "25%     1831.000000  \n",
       "50%     3415.000000  \n",
       "75%    15604.000000  \n",
       "max    39276.000000  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "###获取中国gdp数据\n",
    "data=pd.read_csv(\" data/line_animation.csv\")\n",
    "\n",
    "year=data['time']\n",
    "china=data['china']\n",
    "usa=data['usa']\n",
    "king=data['king']\n",
    "japan=data['japan']\n",
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-21T02:34:20.521593Z",
     "iopub.status.busy": "2022-03-21T02:34:20.520618Z",
     "iopub.status.idle": "2022-03-21T02:34:20.848328Z",
     "shell.execute_reply": "2022-03-21T02:34:20.847532Z",
     "shell.execute_reply.started": "2022-03-21T02:34:20.521550Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/cbook/__init__.py:2064: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
      "  x[:, None]\n",
      "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/axes/_base.py:248: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
      "  x = x[:, np.newaxis]\n",
      "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/axes/_base.py:250: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
      "  y = y[:, np.newaxis]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f68ad138750>]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#创建figure与axes对象\n",
    "fig,axes=plt.subplots()\n",
    "#设定figure图的大小\n",
    "fig.set_size_inches(10,10)\n",
    "\n",
    "#设定axesx轴y轴的数值范围\n",
    "axes.set_xlim(1800,2040)\n",
    "axes.set_ylim(0,40000)\n",
    "###基于plot方法绘图\n",
    "axes.plot(year,china,color='blue')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-14T04:04:32.210610Z",
     "iopub.status.busy": "2022-03-14T04:04:32.210057Z",
     "iopub.status.idle": "2022-03-14T04:04:32.656563Z",
     "shell.execute_reply": "2022-03-14T04:04:32.655610Z",
     "shell.execute_reply.started": "2022-03-14T04:04:32.210571Z"
    }
   },
   "source": [
    "#### 创建figure对象以及对象包含的多个子图\n",
    "fig2,axes2=plt.subplots(2,2)\n",
    "fig2.set_size_inches(5,5)\n",
    "ax1=axes2[0,0]\n",
    "ax1.set_title(\"china\")# 给第一个图赋值title\n",
    "#我们单独给第一个图的x轴y轴设置了不同的数值范围\n",
    "ax1.set_xlim([1800,2040])\n",
    "ax1.set_ylim([0,80000])\n",
    "ax1.plot(year,china,color='blue')\n",
    "#定义第三个字图\n",
    "ax2=axes2[1,0]\n",
    "ax2.set_title(\"usa\")\n",
    "ax2.set_xlim([1800,2040])\n",
    "ax2.set_ylim([0,80000])\n",
    "ax2.plot(year,china,color='red')\n",
    "#定义下一个字图\n",
    "ax3=axes3[1,1]\n",
    "ax3.set_title(\"king\")\n",
    "ax3.set_xlim([1800,2040])\n",
    "ax3.set_ylim([0,80000])\n",
    "ax3.plot(year,china,color='yellow')\n",
    "\n",
    "ax4=axes2[1,0]\n",
    "ax4.set_title(\"japan\")\n",
    "ax4.set_xlim([1800,2040])\n",
    "ax4.set_ylim([0,80000])\n",
    "ax4.plot(year,china,color='red')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-21T05:33:31.232519Z",
     "iopub.status.busy": "2022-03-21T05:33:31.231538Z",
     "iopub.status.idle": "2022-03-21T05:33:31.932046Z",
     "shell.execute_reply": "2022-03-21T05:33:31.931067Z",
     "shell.execute_reply.started": "2022-03-21T05:33:31.232466Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/cbook/__init__.py:2064: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
      "  x[:, None]\n",
      "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/axes/_base.py:248: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
      "  x = x[:, np.newaxis]\n",
      "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/axes/_base.py:250: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
      "  y = y[:, np.newaxis]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fbaa5b86610>]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#### 创建figure对象以及对象包含的多个子图\n",
    "fig2,axes2=plt.subplots(2,2)\n",
    "fig2.set_size_inches(5,5)\n",
    "ax1=axes2[0,0]\n",
    "ax1.set_title(\"china\")# 给第一个图赋值title\n",
    "#我们单独给第一个图的x轴y轴设置了不同的数值范围\n",
    "ax1.set_xlim([1800,2040])\n",
    "ax1.set_ylim([0,80000])\n",
    "ax1.plot(year,china,color='blue')\n",
    "#定义第三个字图\n",
    "ax2=axes2[1,0]\n",
    "ax2.set_title(\"usa\")\n",
    "ax2.set_xlim([1800,2040])\n",
    "ax2.set_ylim([0,80000])\n",
    "ax2.plot(year,usa,color='red')\n",
    "#定义下一个字图\n",
    "ax3=axes2[0,1]\n",
    "ax3.set_title(\"king\")\n",
    "ax3.set_xlim([1800,2040])\n",
    "ax3.set_ylim([0,80000])\n",
    "ax3.plot(year,king,color='yellow')\n",
    "\n",
    "ax4=axes2[1,1]\n",
    "ax4.set_title(\"japan\")\n",
    "ax4.set_xlim([1800,2040])\n",
    "ax4.set_ylim([0,80000])\n",
    "ax4.plot(year,japan,color='green')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-02-28T03:40:39.393495Z",
     "iopub.status.busy": "2022-02-28T03:40:39.392422Z",
     "iopub.status.idle": "2022-02-28T03:40:40.000405Z",
     "shell.execute_reply": "2022-02-28T03:40:39.999289Z",
     "shell.execute_reply.started": "2022-02-28T03:40:39.393437Z"
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-02-28T03:45:03.281572Z",
     "iopub.status.busy": "2022-02-28T03:45:03.280677Z",
     "iopub.status.idle": "2022-02-28T03:45:03.286106Z",
     "shell.execute_reply": "2022-02-28T03:45:03.285214Z",
     "shell.execute_reply.started": "2022-02-28T03:45:03.281527Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "hello world\n"
     ]
    }
   ],
   "source": [
    "import matplotlib\n",
    "hello ='hello world'\n",
    "print (hello)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "请点击[此处](https://ai.baidu.com/docs#/AIStudio_Project_Notebook/a38e5576)查看本环境基本用法.  <br>\n",
    "Please click [here ](https://ai.baidu.com/docs#/AIStudio_Project_Notebook/a38e5576) for more detailed instructions. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-21T05:33:35.655586Z",
     "iopub.status.busy": "2022-03-21T05:33:35.655132Z",
     "iopub.status.idle": "2022-03-21T05:33:36.118038Z",
     "shell.execute_reply": "2022-03-21T05:33:36.117434Z",
     "shell.execute_reply.started": "2022-03-21T05:33:35.655556Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fba9f868b10>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.font_manager import FontProperties\n",
    "## 中文显示问题\n",
    "myfont=FontProperties(fname=r\"/home/aistudio/external-libraries/微软雅黑.ttf\",size=12)\n",
    "#创建figure对象以及对象包含的多个子图\n",
    "fig2,axes2=plt.subplots(2,2)\n",
    "fig2.set_size_inches(5,5)\n",
    "fig2.suptitle(\"各国GDP\",fontproperties=myfont)\n",
    "ax1=axes2[0,0]\n",
    "ax1.set_title(\"中国\",fontproperties=myfont)# 给第一个图赋值titile\n",
    "# 我们单独给第一个图的X轴Y轴设置了不同的数值范围\n",
    "ax1.set_xlim([1800,2040])\n",
    "ax1.set_ylim([0,80000])\n",
    "ax1.plot(year,china,color=\"red\",label=\"GDP\")\n",
    "# 定义第三个图\n",
    "ax2=axes2[1,0]\n",
    "ax2.set_title(\"美国\",fontproperties=myfont)\n",
    "ax2.set_xlim([1800,2040])\n",
    "ax2.set_ylim([0,80000])\n",
    "ax2.plot(year,usa,color='blue')\n",
    "# 加入图例\n",
    "ax1.legend()\n",
    "ax2.legend(('the gdp of usa',),loc='right',fontsize=12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-21T03:01:09.247048Z",
     "iopub.status.busy": "2022-03-21T03:01:09.246047Z",
     "iopub.status.idle": "2022-03-21T03:01:09.388750Z",
     "shell.execute_reply": "2022-03-21T03:01:09.388003Z",
     "shell.execute_reply.started": "2022-03-21T03:01:09.247011Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "x=np.linspace(-2*np.pi,2*np.pi,200)\n",
    "y=np.sin(x)\n",
    "fig3,axes3=plt.subplots()\n",
    "axes3.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-21T03:10:22.017157Z",
     "iopub.status.busy": "2022-03-21T03:10:22.016621Z",
     "iopub.status.idle": "2022-03-21T03:10:22.222129Z",
     "shell.execute_reply": "2022-03-21T03:10:22.221235Z",
     "shell.execute_reply.started": "2022-03-21T03:10:22.017120Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "x=np.linspace(-2*np.pi,2*np.pi,200)\n",
    "y=np.sin(x)\n",
    "fig3,axes3=plt.subplots()\n",
    "axes3.plot(x,y)\n",
    "axes3.spines['top'].set_visible(False)\n",
    "axes3.spines['right'].set_visible(False)\n",
    "axes3.spines['left'].set_position(('data',0))\n",
    "axes3.spines['bottom'].set_position(('data',0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-21T03:34:22.694514Z",
     "iopub.status.busy": "2022-03-21T03:34:22.694019Z",
     "iopub.status.idle": "2022-03-21T03:34:23.060801Z",
     "shell.execute_reply": "2022-03-21T03:34:23.060009Z",
     "shell.execute_reply.started": "2022-03-21T03:34:22.694475Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.patches.FancyArrow at 0x7f68abc083d0>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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a+EoBwzBiUd5xCMTv9ys9hAU8ePgCCqUy/psKnsSr/Oo1fcgVS3j4yGRD71ObttRgfcViMGg79eWLJfzXkUnctrUHdotJ6eEAADodFrxtUxd+dnIGpVJZ6eGQhXODWKjqq+2MtwKpVErpISzg31+ZwNX9HmzsaW/j0+XYttqN4W4nfvzq+YbepzZtqcH6iqXaAFirPD0aQjSdx8Edq5UeygJuv7oXF+NZvHw2qvRQyMK5QSxU9SVt9jKZjNJDqHE2nMYbF+KqS86SJOG9u/rw4ukozkXSdb9PTdpShPUVi9bN3oOHz8NjM+NmBZqyL8etW3pgNUl4+PXGVgqY+uHcIBaq+pI2e2riv45Wkt/+basUHsmVvHtnxYA+9NoFhUfCMMxKzOaKeOzNKRy4ahUsJnWlcKfVhBsGvPjPI5Mo8lIuw6gGdWUKmXG73UoPocZ/Hr2Iq/s9WOOzKz2UK+jvtGPnGi8eOXqx7veoSVuKsL5i0XLN3k+PTyGdK+JdKlslqHJwZx+mE1m8eJpmCwul4dwgFqr6ajfj1YFalmrOz8zitXMzePt29c3qVdm/bRWOnI/h/MxsXa9Xi7ZUYX2ZpfjJaxfQ7bLiugF1FpLfPOxHh9nAS7mC4NwgFqr6kjZ7iYQ6+j1VZ8zesb1X4ZEsTfVczcfeqG92Ty3aUoX1FUupVFJ6CE2RyRfxixMh7N+2CkYFztWuh1JuFrdu7sF/HeWlXBFwbhALVX1Jmz218F9HJrGl142BgLJd7pdjsMuJDd1OPFqn2WMYpv08czKE2XxRNb31luKdV/cilMzhhfGw0kNhGAbEzZ7NZlN6CJiKZ/Dy2SjeoeIl3Cr7t63CofEIIqmVGyyrQVvKsL5ikSR1zoqtxONvTsFpNWHvoDqXcIFK7N6yqQsWkwFPvDmt9HDIwblBLFT1ZbMnmEffuIhyGThwlfrN3tu3r0KpDDxxbGrF16pBW8qwvmLRotkrlcp44tg03jZnpNSKzWaD3WLCDUN+/PT4FNkaKKXg3CAWqvqqN2PIgBoONH7i2DQGAw4Md6unkfJSbFvtRp/XVlfdnhq0pQzrKxYt1uwdnphBKJnFr6h8Cbcau7du6cGZcBqngjSb1CoF5waxUNWXtNlTmnSugOfHwvilzd1KD6UuJEnCr2zrwS9GQ0hlC0oPh2GYeTz+5hRMBgm3bNJGPqnmvZ/WsVLAMIxYSJs9k0nZMyOfPRlGrlDCPo2YPQD4la2rkCuU8H9HQ8u+TmltqcP6ikWLy7hPvDmF6wZ98NjMSg9lWaqx2+e1YUuvGz89znV7csK5QSxU9SVt9jo7OxX9/CdHpuGwGLFnvU/RcTTC7vWdcFpN+PmJ4LKvU1pb6rC+YtFaU+XToRRGp5P45S3qXsIFFsburZu78fKZKGbSK2/6YuqDc4NYqOqrrYzXIOGwctv+y+Uynjo+jRs3BFRdTH05ZqMBbx324xcngssWViuprR5gfcVSLBaVHkJDVDdNacHszY/dW7d0o1gqr/jwyNQP5waxUNVXOy6kCZQswj4xlcSFWAb7NFJfM59bNnXj/MwsTk4nl3yNFgvctQTry8zn5yeC2NDtVOVxi5czP3Z39HsRcFrwxDFeypULzg1ioaovabOnJE/O1aloqV6vyts2dgEAP40zjArI5Is4NB7BzXPfSy1hMEjYt6kbT41MI1+k+T9RhtECpM1eIBBQ7LN/NjKNrb1u9Lg7FBtDs6z22rCxx4mnRpY2e0pqqwdYX7FoqWbv0HgE2UIJN23QRkxcHrv7NncjkSng8LkZhUZEC84NYqGqr3YyXhModcZdLJ3Hy2ei2LdZe0/iVd62sQuHxiNLtmChen6gWmB9xaKlRr+/OBGExWTAdQPqPTVjPpfH7g1DfkgSVtzhz9QH5waxUNWXtNnLZrOKfO7TJ0MolsqarNercsumbuSKJTw/tnixqlLa6gXWVyyaMnujQbxlvQ82i1HpodTF5bHrtVtwdZ8Hz5xksycHnBvEQlVf0mZPKZ4+GYLTasLONV6lh9I0u9d3wmY2LruUyzCMWC7GMjgxlcTNG7W9tHTjhgAOn5tBPJNXeigMo0tImz2Px6PI5z57KoS9gz6YjNqV12oy4oYhP546Mb3oLIhS2uoF1lcsWqnZ+8Vo5WHrpg3aKQlZLHZvHO5CsVTG86dotrVoJ5wbxEJVX21kvCZRopfWRDSNM+E0bhjS9pM4ANy8sQvnIrM4F5m94nda61OmNVhfBqjU63W7rNi8Sv1na1dZLHavWeeFzWzE07yU2zKcG8RCVV/SZi+ZXLpPnCiePVl5cr1RIzvnluOGoUpB+HNjVyZoJbTVE6yvWLTQS6tYKuPpkyHctKFLU8e7LRa7VpMR1w368DRv0mgZzg1ioaovabOnBE+fDKHLZcWGbqfSQ2mZ4W4nAk4rnuWlF4ZpO29ciGEmndd8vV6VG4cDGAulcH7mypUChmHEQtrs2e3t7TZfLpfx7KnwXKsB7TyJL4UkSbhhyI9nT4WvqNtrt7Z6g/UVixa+n8/NPWRdP6SNlitVlordat3h06O86asVODeIhaq+pM2e1Wpt6+edmEoilMzircM0nsSBylJuMJHFqWBqwc/bra3eYH3FogmzNxbGcLcT3S5tNWZfKnY39jjR7bJyv70W4dwgFqr6kjZ70Wi0rZ9XLT6mZfYq/5bnTi1M0O3WVm+wvmJRe81eoVjCi+MR7B30KT2UhlkqdiVJwo3DATy3yEoBUz+cG8RCVV/SZq/dPHsyhPV+O/q8NqWHIhtrfDb0eW1ct8cwbeTohThSuSL2DmprCXcl9g76EU7lcHKaZhE8w6gV0mbPbDa37bMKxRJeGI+QmtUDKk/j1w/58fxYGKXSpafxdmqrR1hfsah9Gbd6co0Wzd5ysfuWgcpM5fPjkXYNhxycG8RCVV/SZs/rbd8JFkcvxJHMFjRXTF0PNwz5EU3ncfzipTMD26mtHmF9xaL2psrPnQpjw9xueK2xXOyu89vR47biEJu9puHcIBaq+qo747VIKNS+QuBD45Un8eqTKyWqBvbZeXV77dRWj7C+YlFz49R8sYSXTkc0OasHLB+7kiThugE/Xhjjur1m4dwgFqr6kjZ77Uwmh8ajGAg4NLdzrh56PTYMBBy1VhCAtg6S1yKsr345ej6GVK6o2VWClWL3ukEfphNZnA6n2zQiWnBuEAtVfUmbvXZRKpXx4ukI3rKe3qxelb2DPrx4OrKgbo9hGPl5bozuKgEAXDdQMbEvjPGmL4ZpF6TNXiDQns0SJ6YTiM3mySZnANi9zod4poDRuV107dJWr7C+YlFzzd7zYxFs7NFmvR6wcuwOdTkQcFrxAtftNQXnBrFQ1Ve9GU8G4vF4Wz7nxbmkRdns7ZmbtTx0uvJvbZe2eoX1FYtal2q0Xq8HrBy7lbo9H9ftNQnnBrFQ1Ze02cvlcm35nBfGI+j1dKC/k05/vctZ47Ohx23FS3Nmr13a6hXWVyxqNRnHJuNI54qafnCsJ3avG/ThQiyDiSifk9sonBvEQlVf0mavHZTLZRwaj2DPep/qe3e1giRJ2L3eV5vFZBhGfl46Xenev3udds1ePVTr9p7nuj2GaQukzZ7H4xH+GWcjaUwnspp+Eq+XPes6cSGWwfmZ2bZoq2dYX7GotWbvpTMR9HltWOXR7q7+emJ3Q7cTnXYz99trAs4NYqGqrzoznkwUCgXhn1EtMr5OD2Zv7t/44nikLdrqGdZXLGpcxi2Xy3jpdBS713cqPZSWqCd2DQYJ167z4eWzNM8hFQnnBrFQ1Ze02UulUsI/49B4BJ12M4a7ncI/S2k2r3LDaTXhxdORtmirZ1hfsajR7E1EZzGdyGL3Om2bvXpj99p1nRgLphBJ0ayREgXnBrFQ1Ze02WsHL52OYDfxer0qRoOEa9Z14sXTvPTCMHJT/V7tJtyvcz7XzpnaV3l2j2GEQ9rsORwOodcPJStd4LX+JN4Ib1nfiRNTSeQNFqWHQhrRsat31Phw9tKZKFxWEzb2uJQeSkvUG7tX93tgMkh46QybvUbg3CAWqvqSNntms1no9V89OwMAuEZHZq8663BkkuZUt1oQHbt6R41m7+XTUexa1wmjQX1ja4R6Y7fDbMS2Pg9eZrPXEJwbxEJVX9Jmb2ZmRuj1Xzkbhckg4ao+mrt3FmPnGi/MRgnPjFxUeiikER27eqdUKik9hAXE0nmcmE6QWCVoJHavXduJ187NIF9U1/1QM5wbxEJVX9JmTzSvnIli22o3OsxGpYfSNjrMRmxd7cHRyaTSQ2EI8cgjj2DTpk0YHh7Gl770pSt+/6lPfQo7d+7Ezp07sXHjRni93trvjEZj7XcHDx5s57Bl45WzUZTLIGH2GuHadZ3IFkp48wLNUwsYRi2YlB6ASCwWcXVlhWIJr0/E8ME9a4R9hlrZtcaL+188i0KxBJORnxdEIDJ21UaxWMRdd92Fxx9/HP39/dizZw8OHjyIrVu31l7z5S9/ufbnv//7v8err75a+7vNZsPhw4cb+ky1LeO+dCYCo0HCzrXelV+schqJ3WqbmZfPRLFjjfb/7e1AT7lBCajqS/r/1CKbIx6/mMBsvqirer0qu9Z6kcmXMDKVUHooZKHa2HMxDh06hOHhYQwODsJiseCOO+7Agw8+uOTrv//97+NDH/pQS5+ptqbKL8+tEtgt2n/+biR2e9wd6PPauN9eA+gpNygBVX3VlfFkJhQKCbv2K3PJ6RoCT+KNcs3aassEmrUNakBk7KqN8+fPY82aSzPk/f39OH/+/KKvPXPmDMbHx/FLv/RLtZ9lMhns3r0be/fuxY9//OO6PrNYLLY2aBmprhLsIjKz1WjsXruuEy+fjqqy96Ea0VNuUAKq+jb0GJlOp2sNBx0OB0wmE2KxGIDK1Kfb7a4JJUkSAoEAZmZmkM/nAQCdnZ3IZrNIp9MAAKfTCaPRWLuG1WqFy+WqXcNgMMDv9yMajda6Wvt8PszOzmJ2tnKAtsvlgiRJiMcrNR8dHR1wOBwIh8OIRCIwGAzw+XyIRCK1BO/3+5FKpZDJZAAAbrcb5XIZiURlpspms8FmsyESqfS9MplM6OzsRDgcrhV2v3w6ii6nBZZ8EsFgCh6PB8ViEclkpZbNbrfDarUiGq2YQrPZDK/Xi1AoVEtqgUAA8Xi8dvCyx+NBoVBYoLHZbK4VjFosFng8nto1qhrHYrHaNbxeL/L5vND75LZY4LOb8NzoRewfsrd8n4BK3ZWI+xQIBJBIJJDNZmsaa+E+JRIJ+P1+VX2fRN2neDyOcrmMeDyObDaLeDyOYrGI2dnZK+7TN7/5Tbzzne9EIpGo3adXXnkFvb29iMfj2LdvH1avXo2BgYEr7tMDDzyA++67DyaTCdFoFLFYTBXfp9MzeaRzRQx1GhEMBlV7n+r9PhUKBQSDwbq/Tzv6XXjotQs4OnYeq9xW1eY9tXyf0uk0Ojs7SeY9NdynSCQCq9Wqmu/TSvepq6sL9SA1+DSlqUevUCiEQCAg5No3/X9PYluvB1//zWuFXF/t/Nb/fgYT8Tye/KNblB4KSUTGrtp47rnn8PnPfx6PPvooAOCee+4BAHz2s5+94rW7du3CP/zDP+CGG25Y9Fof+chHcPvtt+N973vfkp8nSRI2btyIkZERGUbfOv/6wll87j+O4Kk/vgXrA9rv8dVo7B49H8Ptf/80/u6OnXj3zj6BI6OBnnKDEmhQ37oKkEkv44q6YcFEFucis7hmHY1ll2a4brgHY8EUZtJ81JEINJZsWmLPnj0YHR3F+Pg4crkc7r///kV31R4/fhzRaBTXX3997WfRaLT2VBwKhfDMM88s2NixFEajenbQHz4XRafdjHV+u9JDkYVGY3fzKhc6zAa8di4maES00FNuUAKq+pI2e9VpXbmp1utdq8PNGVU2+SuNJw+f47o9EYiKXTViMplw7733Yv/+/diyZQs+8IEPYNu2bbj77rvx0EMP1V53//3344477liwk/bYsWPYvXs3duzYgX379uEzn/lMXWZPTX32Dp+bwY41XtXtEG6WRmPXZDRg+2oPXpvgXFIPesoNSkBVX+1v/VqGap2A3Lx6dgZmo4Rtq2nu2qmHDX4rDFJFi1s8ID09AAAgAElEQVQ2dSs9HHKIil21cuDAARw4cGDBz77whS8s+PvnP//5K953ww034MiRIw1/nlo2AyQyeYxOJ3Hgql6lhyIbzcTujjVefO/5M8gXSzBzO6dl0VtuaDdU9eVvVRMcPhfF1l59NVO+HLvFiE2r3HiVZ/YYpmmOTMRQLldOptEzO9d4kS2UMHKR2zkxjAhIm735Xfblolgq48hETPcNQL1eL3at9eLw2ShKJXXMklBCROwyl1BLn73qwxIls9dM7Fb//byUuzKcG8RCVV91ZDxBVLdqy8lYMIlUroir+2kGRL3k83nsWuNFPFPAWCil9HDIISJ2mUuoZRn38LkZDAQc8NrpdO1vJnb7O23wOSw4zL07V4Rzg1io6kva7FV7+cjJaxOV4s0d/fqt1wMq2u6aayj9Kne/lx0RsctcQg1mr1wu4/C5GVKzekBzsStJEnb08yaNeuDcIBaq+pI2eyJ47dwMnFYTBrucSg9FcQYDTjitJhw5T3P3EsOI5EIsg2AiS87sNcuONV6MTieRzBaUHgrDkIO02XM45G9Q+vrEDLb3uWE00GiT0CwOhwMGg4Ttfe7abCcjHyJil7mEGtqcVJcsqZm9ZmN3xxovyuXKphVmaTg3iIWqvqTNnskkb2eZbKGIY5MJ7NB5vR5wSdur+704NhlHrqCevmUUkDt2mYWowey9NjEDi8mALb1upYciK83G7s5+3qRRD5wbxEJVX9JmT+7miMcnE8gVS7rfiQtc0vbqfg9yhRJOTHHLBDmh2thTLaihqfLrEzPY0uuGxUQrDTcbu50OC9b57XiN2zktC+cGsVDVl1aWEczrc0+cV+t8c8Z8ru6rGN/XeemFYeqmVCrj6Pk4ru7jXDKfHf1eNnsMIwDSZs9ikbedwWsTMfgdFvR5bbJeV4tUtV3js8FrN9eMMCMPcscusxCll3HHwykkswVcRdDstRK7O9Z4cSGWwXQ8I+OIaMG5QSxU9SVt9txueWthXiN2hmUrVLWVJAlX9Xl4Zk9m5I5dZiFKf4ePzu1gv4rgKkErsVttacX5ZGk4N4iFqr6kzV4oFJLtWslsASeDSV7CnWO+tlf3ezAylUAmX1RwRLSQM3aZK1G6Zu/1iRisJgM2dNNr4dRK7G7pdUOSwO2cloFzg1io6kva7MlJ9QxL3ol7JVf3e1EslfHmZFzpoTCMJjgyEcPW1W6YjJyC5+OwmjDU5azNfDIMIw+kM42cSzVHzldq0iguuzTDfG2rs52vc2G1bCi9zMiIo1gq440LMbKbM1qN3av6PDyztwycG8RCVV/SZi8QCMh2raPn41jt6UDAaZXtmlpmvrar3B3oclnxOido2ZAzdpkrMRqNin32eKhyvvZVRFcJWo3d7X0eTCeyvEljCTg3iIWqvqTN3syMfDNNRy/EsI3ok3gzzNdWkiRczZs0ZEXO2GWuRMmaver3hOJOXKD12K3qcvQC55PF4NwgFqr6kjZ7+XxeluskswWMh1LYvppmcm6Gy7W9ut+LU0E+11Iu5IpdZnHK5bJin33kfAw2sxFDXTSPZWo1dretntukMcE1wIvBuUEsVPUlbfbk4thkHOUycFU/zS3ZcrC9z41yuaIVwzBLc2Qihm28OWNJHFYTBgMOrttjGBkhnW06OztluU71YG6e2bvE5dpury69cIKWBblil1kcg0GZ1FfZnBGvfV8oIkfsXtXn4VyyBJwbxEJVX9JmL5vNynKdoxdi6HJZ0e3ukOV6FLhc226XFQGnBUfP88yeHMgVu8ziKLWMeyqYxGy+SLpfpxyxu73Pg4vxDIIJ/h5cDucGsVDVl7TZS6fTslznjfNxbF/NS7jzuVxbSZKwbbUHb3BRtSzIFbvM4ihl9o4Q35wByBO7V/FKwZJwbhALVX1Jmz05mM0VMTqdIJ2c5WLbajdGp5N8kgbDLMEbF+LoMBsw2EXv5Aw5qXY+4Lo9hpEH0mbP6Ww9oR6/GEepDG67chmLabu9z4NiqYwTUwkFRkQLOWKXWRqlavbeuBDD5lVuGA00G7cC8sSukzdpLAnnBrFQ1Ze02ZOjcWp1GYFyQXUzLKbttrmlbq7bax0lm/4yYiiXK0cKbiNeEiJX7G7nTRqLwrlBLFT1JW32YrHWE8XR83F02s1Y7eHNGfNZTNu1PjtcHSau25MBOWKXWRolmiqfi8wikSlgG/Fd/XLF7vY+NyZjGURSOVmuRwXODWKhqi9psycHRy/EsL3PQ/a8PDmRJAlbe904eoFn9hjmcqoPQdRn9uSiaoq5dyfDtA5ps2e1tnaObbZQxImpBC/hLsJS2m7v8+D4ZByFonLHUVGg1dhllkeJh7c3LsRhNEjYtMrV9s9uJ3LF7pbeiinmlYKFcG4QC1V9SZs9l6u1pDo6lUS+WOYn8UVYStttq93IFko4FUy1eUS0aDV2meVRwuy9ORnHcJcTHWaaNUFV5Ipdn8OCXk8H3uSVggVwbhALVX1Jm71QKNTS+6tJhnqNTTMspW11FpSfxluj1dhllkeJmr03LsR08eAoZ+xu7XXjTV7GXQDnBrFQ1Ze02WuVNyfjsFuMWOezKz0UzTAYcMBqMvCOXIaZRyiZxVQ8i606MHtysm21G6eCKe7dyTAtQtrstdpL683JODavcsFAuCdWsyylrclowJZeN47yzF5LKNUHjhHDG3OrBHowe3LG7tbVbhRLZYxc5N6dVTg3iIWqvjT/VXP4/f6m31sul3FsMq6L5NwMy2m7bbUbxybjih1JRYFWYpdZmXb30qrtxO2lXxIiZ+xundOLl3IvwblBLFT1JW32otFo0++diFZ6YlV3hDELWU7bLb1uJDIFTERn2zgiWrQSu8zKtLtm740LcfR32uCxm9v6uUogZ+yu8dngspp4k8Y8ODeIhaq+pM1eoVBo+r3VJ8mtbPYWZTltqwaZ+2M1Tyuxy6xMu2ed37xA/+SMKnLGriRJ2LKaN2nMh3ODWKjqS9rstcKxyTgMErB5lT4StJxsXuWCJAHHJrnOhmFS2QJOh1O8q79JtvZWykKKJS4LYZhmIW32fD5f0+9980Ic6wMO2Cy0e2I1y3LaOqwmrPPZeWavBVqJXWZl2lmEffxiAuWyflYJ5I7dravdSOeKOBPm3p0A5wbRUNWXtNmbnW2+ZuzNybhuknMzrKTtll43jl1ks9csrcQuszLtXMatPvRs7qXZrPVy5I7d6vI3L+VW4NwgFqr6stlbhNhsHhPRWd6csQz1mL0z4TSSWZr1D6KhmnDUQjvN3vGLcbg6TOjz2tr2mUoid+xu6HbBbJR4k8YcnBvEQlVf0mavWY5P6qcnliiqRnmEZ/cYnXNsMoEtq9yKHNFGAYvJgOFuV61XIcMwjUPa7DV7xl11uWAbz+wtyUrabplbsnqTN2k0BdXzGdVCu2r2SnMNgbfoZAkXEBO7W3pd3Fh5Ds4NYqGqL2mz1+yT9LHJOPwOC7pcVplHRIeVtO3z2uDuMPEmjSbhWSAaTERnkcwWsFlHD44iYnfLKjcuxjOIpnKyX1trcG4QC1V9SZu9eLw5o/Hm3MkZVG+6HKykrSRJlU0abPaaotnYZeqjXU2Vq5uU9FT/KyJ2q5tbjvPsHucGwVDVl7TZa4ZCsYQTU0ldJWdRbOl1Y+RiAiXuj8XolGOTcUgSsLHHqfRQNE213+lxrgFmmKYgbfY6Ojoafs/pcBq5Qgmbemiu28tFPdpu7Z3rjxVJt2FEtGgmdpn6ades/fHJBAb8DtgtprZ8nhoQEbtdLisCTguOcw0w5wbBUNWXtNlzOBwNv6daBLxpFZu95ahHWz42rXmaiV2mftpl9o5djOumv14VUbG7aZWLZ/bAuUE0VPUlbfbC4XDD7xmZSsAgAcPdvOyyHPVou6HHCaOB+2M1QzOxy9RPO2r2UtkCzoTT2KKzIxdFxe7mVW6MTCV0f2wa5waxUNWXtNlrhpGLlWPSOsx8TFqrdJiNGAw4+Gmc0SXVzQR62okrks2rXMjkS3xsGsM0AWmzZzQ2bthGLia4Xq8O6tV24yoXTkwlBY+GHs3ELqMujtd24uorn4iK3WpZiN535HJuEAtVfUmbvUYPNJ6d20zA9XorU6+2m3pcOBtJI53jY9Mageph3GqhHQn92KS+jkmrIip2h7udMEiXTjjSK5wbxEJVX9JmLxKJNPT60ekEymXwzF4d1KvtxjktR3l2ryEajV2mMYrFovDPOK7TY9JExW6H2YjBLieO6Xxmj3ODWKjqS9rsNZrQeSdu/dSrbVXLkSl9J+hGaYcZYcRRLleOSdNjLhEZu5t5Ry7nBsFQ1Ze02WuUkYsJWE0GrPPT3HqtBGt9dnSYDTih86dxRl9MxjJIZAvYqEOzJ5ItvW6ci8wikckrPRSG0RSkzZ7f72/o9SNTiVq7EGZ56tXWaJCwodvFM3sN0mjsMo1hMIhNfSfm4l2PJSEiY3fznHk+oeN8wrlBLFT1JW32UqnGtuhXduJym4R6aETbjT2u2hI5Ux+Nxi7TGOWy2F5tVTOix2PSRMZutY3Nmzo+SYNzg1io6kva7GUymbpfG03lMJ3IYtMq/SXnZmhE202rnJhOZBFN5QSOiBaN6Ms0jmizN3IxiW6XFV67RejnqBGRsbva0wGX1YRRHc/scW4QC1V9SZu9RqguM27SWbf7dlDdkavnpRdGX5yY0ufmDNFIkoQNPU5eKWCYBiFt9tzu+o1bbSeuDmtsmqERbTdxnU3DNKIv0zgia/ZKpTJGpxO1hxy9ITp2N/a4MDqt31ZOnBvEQlVf0mavkaWakakEPDYzetxWgSOiQyParnJ3wNVh4k0aDSB6mZERx7loGpl8SZf1eoD42N3Y40IklUMomRX6OWqFc4NYqOpL2uwlEvWbi+oxaXprgNosjWgrSRI29bhw4qJ+n8YbpRF9mcYplUrCrl1dJdDrzJ7o2K2Vheh0KZdzg1io6kva7NVLuVzGCZ02QG0Xm+aaoVJ9amKYKtUlxg06NXuiqc6YclkIw9QPabNns9V3JuUFboDaMPVqW2XTKhfimQKm4vpcemmURvVlGkPkDP7IxQT6vDY4rSZhn6FmRMdul8sKj82MEzqt2+PcIBaq+rLZAzAyd/zOZjZ7ddPoF6K69MJ1e/VBNeGoBZFmT+87cUXH7qWyEH3mEs4NYqGqL2mzV++BxiNztWR6rbFphkYPi9Z7nU2jUD2MWy2IqtnLF0s4FUzqOpe0I3Y39DhxYiqhy7IQzg1ioaovabNXLyMX4+j1dMBjMys9FLL4HBZ0uaw8s8eQ5kw4hXyxzM3ZBbOxp1IWMp3gshCGqQfSZs9kqq9mZmQqqetll2aoV9v5bOpxcVF1nTSjL1M/opZxq6sEG7r1m0/aEbu1shAdrhRwbhALVX1Jm73Ozs4VX5MvlnBqOsnNlBukHm0vZ+Oc2SuV9Lf00ijN6MvUj6imyiNTCRgkYLhbvzN77YhdPe/I5dwgFqr6kjZ74XB4xdecCaeQK5Z4Zq9B6tH2cjavciGTL+FcNC1gRLRoRl8t88gjj2DTpk0YHh7Gl770pSt+/+1vfxtdXV3YuXMndu7ciW9+85u1333nO9/Bhg0bsGHDBnznO9+p6/OKxaJsY5/P6FQC6/0OdJiNQq6vBdoRu36nFX6HBaNT+tuRq7fc0G6o6ktzvnKOeoqwj+u8AWqzNFPgXm1tc/xiAuv8DrmHRAqRTX/VRrFYxF133YXHH38c/f392LNnDw4ePIitW7cueN0HP/hB3HvvvQt+FolE8Bd/8Rd46aWXIEkSrr32Whw8eFCxp/ORqQQ26PTkjCrtit2NPS5d1gDrKTcoAVV9Sc/s1cPIxQSMBknXyy7tYsOcxrwjl5nPoUOHMDw8jMHBQVgsFtxxxx148MEH63rvo48+ittuuw0+nw+dnZ247bbb8Mgjjwge8eJk8kWcDqW4JKRNbOxx4uR0Upc7chmmURqa2Uun00ilUgAAh8MBk8mEWCwGALBYLHC73QiFQgAqBdCBQAAzMzPI5/MAKmvh2WwW6XRlGc/pdMJoNNauYbVa4XK5atcwGAzw+/2IRqMoFAoAAJ/Ph9nZWczOzgIAXK7KEWfxeKVXXkdHBxwOB8LhMMrlMiKRCHw+HyKRSG3pxu/3I5VKIZPJ4MjZMNb5bEAxj+BMZcu1zWaDzWarbcE2mUzo7OxEOByuuf5AIIBEIoFstrIbzOPxoFgsIpmsLCvY7XZYrVZEo1EAgNlshtfrRSgUqiWnQCCAeDyOXC5Xu0ahUFigsdlsxszMTE1jj8dTu0ZV41gsVruG1+tFPp8Xfp/8fj+CwWBD98kkSVjtseL1syEkEqtq9wkAjEbjsvcJqBxQXS6Xa8fZUL5PZrMZ5XJZVd8nUffp2LFj6O/vRzweRzabhdvtxtGjRzE7O1u7T9lsFj/60Y/w5JNPYmhoCPfccw+2b9+OEydOwOfzIRgMIhAIIBAIYGRkBMFg8Ir79MADD+C+++6r6RyLxWT9Po1Mp1AqV2awKd6ner9PXq+3lhtEfp/63SYkswUcHTuPzWt72pL31HCfOjo6UCwWSeY9NdynqtZq+T6tdJ+6urpQD1KDT0WaeoSKx+Nwu93LvuZt/+tn2Lbaja/++rVtGhUN6tF2MT76nRdxNpLGY596m4BR0aFZfbXID3/4QzzyyCO1Orx//ud/xgsvvLBgyTYcDsPpdMJqteIb3/gGHnjgATz55JP4q7/6K2QyGfzZn/0ZAOAv//IvYbPZ8Md//MdLfp4kSdi0aROOHz8u67/jx6+exx88cBiPfepmXZeFtCt2Xzwdwfu//hzu+8ge7NvcLfzz1IKecoMSaFDfuloLkF7GrbrlpUjnCjgbSWNTj6ZurCpYSdul2NjjwlgwhVyBZl2EXDSrrxbp6+vDuXPnan+fmJhAX1/fgtf4/X5YrVYAwEc/+lG8/PLLdb93MUQs/Y1MJWA2Sliv83rUdsXuxrn2Nnrbkaun3KAEVPUlbfZWYnQqiXIZ3AC1jWxa5UKhVMZ4KKX0UBiVsGfPHoyOjmJ8fBy5XA73338/Dh48uOA1k5OTtT8/9NBD2LJlCwBg//79eOyxxxCNRhGNRvHYY49h//79bR1/lRMXExgMOGEx6Tqttg2P3YxulxUndLgjl2EahfRuXI/Hs+zvqzu5Nq3imb1GWUnbpZh/Ri63u1maZvXVIiaTCffeey/279+PYrGIO++8E9u2bcPdd9+N3bt34+DBg/jKV76Chx56CCaTCT6fD9/+9rcBVGpv/vzP/xx79uwBANx9993w+XwrfqaIPnsjUwnsWkuzR1cjtDN2N+qwUbuecoMSUNWXtNlbqZfW6FQCVpMBa332No2IDs32KRvqcsJkkDByMQ7sWC3zqOggqg+cWjlw4AAOHDiw4Gdf+MIXan++5557cM899yz63jvvvBN33nmn0PGtRCpbwER0Fh/cvUbRcaiBdsbuxh4Xvn/oLEqlMgwGMaeiqA295YZ2Q1Vf0usN1R0tSzE6ncRglxNGnSQJOVlJ26WwmAwYCDhqx0oxi9Osvkx9yN1La3S6cr828mx1W2N3Y48Ts/kiJqKzbftMpeHcIBaq+pI2eysxOpWs9X5j2sfGVfpbemFoU+0dyT322suGHn1u0mCYRiFt9uz2pZdn07kCzs/McjPlJllO25XY1OPC2Uga6VxBxhHRohV9mZWRJHln80/MlYSs4ZKQtsZu9YxcPZ2kwblBLFT1JW32qq0aFuPUdGU3KM/sNcdy2q5ENUHr8VzLemlFX2Zl5DZ71WPSuCSkvbHr6jBjtacDozoye5wbxEJVX9Jmr9p1ejFOBivJgWf2mmM5bVdieK4/1slpNntL0Yq+zMrIXbN3Yiqh60bK82l37G7ocemq/QrnBrFQ1Ze02VuO0akkTAYJ63TeAFUJ1vntMBkknAzqJ0EzdIml85iKZ7leTyE2rXLhZDCJYklTBzwxTFshbfbMZvOSvzs5ncT6gIMboDbJctqu+F6jAesDDp7ZW4ZW9GVWRs5l3BPTlVUCntmr0O7Y3dDtRK5QwpmwPhq1c24QC1V9STsdr9e75O9OTicx3MVLuM2ynLb1MNzlxCk2e0vSqr7M8sjZVHlkbifuhh7OJ0D7Y3djbUeuPvIJ5waxUNWXtNkLhUKL/jxbKOJ0OMXJuQWW0rZeNvQ4cTqcQrZAs4Flq7SqL7M8cjZOPTmdhN1iRJ/XJts1tUy7Y7eax/XSfoVzg1io6kva7C112PnpUBqlMm/OaIVWD5If7naiVK7cC+ZKWtWXaR+ngkkMdTll3+GrVdodu3aLCWt8Nt2YPc4NYqGqL2mztxSj07wTV2mG5pbQuW6P0Tonp5OcSxRmY7eLWzkxzDKQNnuBQGDRn5+cTkKSLhkOpnGW0rZeKjMhbPaWolV9meWRq2YvmS1gMpZhszcPJWJ3Q48LY6Ek8kV5W+qoEc4NYqGqL2mzF4/HF/356HQSazrt6DAb2zwiOiylbb3YLEb0d9q4/coStKovszxyLdVUNxnxg+MllIjdDd1O5ItlnI3QLwvh3CAWqvqSNnu5XG7Rn5/iZZeWWUrbRhjucuqq830jyKEvszRymb3qzDTnk0soEbtV/fWwUsC5QSxU9SVt9hajUCxhLJjiY9JUwHC3E2OhFDdDZTTLyWC1OTvN8zS1wmBXpTm+HswewzQDabPn8Xiu+NnZSBq5YomfxFtkMW0bZXiuGepElP7SS6PIoS+zNHLV7FWbs5uNpFNpQygRu64OM1a5O3TRu5Nzg1io6ks6QxUKhSt+xssu8rCYto3CZ+QujRz6MksjZ80eN2dfiFKxO9ztxCkd1ABzbhALVX1Jm71U6srjc0bZ7MnCYto2SvUejLLZuwI59GWWRg6zlyuUcCaS5lxyGUrF7lCXA6eCKbJ90qpwbhALVX1Jm73FODWdxCp3B1wdNM+/0xIemxldLivP7DGa5HS4Um/KZk8dDHc7kcwWcDGeUXooDKM6SJs9h8Nxxc9Gp5N8TJoMLKZtMwx3OdnsLYJc+jKLI8dpF1wSsjhKxe7Q3H04NU1zZqYK5waxUNWXtNkzmxfO3pVK5drRRkxrXK5ts2zoceLUdJL80kujyKUvszhymL3qZoDqTlCmglKxO1w7lYd2OyfODWKhqi9pszczM7Pg7xdis0jnijyzJwOXa9ssw91OJLIFTMWzslyPCnLpyyxOqdT6SQsng0n0eW2wW0wyjIgOSsVul8sKV4eJfKN2zg1ioaovabN3ObXNGTyzpxqG+YxcRqPwmbjqQpKkyo5c4su4DNMMpM2exWJZ8PfqssuGHpcSwyHF5do2y6XO97SXXhpFLn2ZxWl1GbdaEsJm70qUjN2hLif5mT3ODWKhqi9ps3d5c8TRqST8Dgt8Dpo3s53I1Xiyy2WFWwdLL41CtbGnWmi1qfL5mVlk8tycfTGUjN3hbieCiSxis3nFxiAazg1ioaovabMXCoUW/P1kMFnbscW0xuXaNkt16YWXcRcil77M4hSLxZbeX304YbN3JUrGrh7KQjg3iIWqvqTN3vwdnuVyGaNTCT4TVybk3D3LZu9KeHeyujnF9b9LomTsVs035ZM0ODeIhaq+pM3e/LqcYDKLeKbAT+IyIUfriirD3U6EkjnMpHOyXVPryKkvIz8npyslIZ1cEnIFSsZuf6cNFqOB9Bm5nBvEQlVf0mYvEAjU/nxyam5zRjdvzpCD+dq2ygY+I/cK5NSXuRKj0djS+09Oc0nIUigZuyajAQMBB+lcwrlBLFT1JW32YrFY7c98Jq68zNe2VS7tyKWboBtFTn2ZK2mlz165XMZJ3om7JErH7nC3k/QyrtL6UoeqvqTNXi53aVnw5HQSLqsJPW6rgiOiw3xtW6XPa0OH2VAz5Iy8+jJX0kpdTjiVw0w6z/V6S6B07A51OXA2kkYm39omHLWitL7UoaovabM3n9HpBIa6nWTX47WMwSBhMMCbNBhtwGfiqpuhbidKZeB0mJsrM0wV0mbP6/XW/nxyOsU7cWVkvrZysKGHzd585NaXWUgrffbY7C2P0rFb25FL9CQNpfWlDlV9SZu9fL7SWDOWziOUzHJylpGqtnIx3OXE+ZlZpHMFWa+rVeTWl1lIK8u4J6eTcFiM6PV0yDgiOigdu4MBJySJbg2w0vpSh6q+pM1eKlV5sqs2QB3iGhvZqGorF9SfxhtFbn2ZhbRi9k7NNWfnkpDFUTp2bRYj+rw2sqfyKK0vdajqS9rsVRmb+9IPdjkUHgmzFLUduUE+I5dRNyenk7w5Q+Vwo3aGWQhps+dwVMzdqWAKZqOENT67wiOiQ1VbuVjnd8BkkDhBzyG3vsxCmp2VS2YLmIxluMfeMqghdoe6nBgLJlEq0TsNQQ36UoaqvqTNnslkAlCZ2Vvrs8NsJP3PbStVbeXCYjJgnd/OZm8OufVlFtKs2TvFmzNWRA2xO9ztRLZQwvmZWaWHIjtq0JcyVPUl7X6qzRFPBZNcryczIhpPDnU5cSpIs16iUag29lQLzTZVrj6McD5ZGjXELuVG7WrQlzJU9SVt9gAgXyzhbCSNQU7Oqmewy4kz4RQKxeZPN2AYkZwMJmEySFjn55IQNVM145RP0mCYRiBt9iwWC85F0sgXyxjizRmyYrHIfwD8YJcD+WIZE1F6Sy+NIkJf5hLNLuOenE5ifcDBJSHLoIbY9Tks8DksJGf21KAvZajqSzpjud1ujM0tC3JBtby43W7Zr8lP45cQoS9ziVZq9ngn7vKoJXaHu2juyFWLvlShqi9psxcKhWrGYSjACVpOQqGQ7Neszr6Ocd2eEH2ZSzRTs5crlHAmkubNGSugltgd6naQfHBUi75UoaovabMHVIxDwGmBx25WeijMCnjtlaWXsRC9BM1on7ORNIqlMvfr1AhDXU5E03mEk1mlh8IwikPa7EmShFPBJAZ5Vk92RJ0eMNTl4F0BwOAAACAASURBVFM0IE5fpnkuNWfnfLIcaoldqjty1aIvVajqS9rsBQIBjIVSGOrmJ3G5CQQCQq47GHDyzB7E6ctUMBqNDb9nLFR5COGZveVRS+xeOpWHVj5Ri75UoaovabN3ejKISCrHM3sCmJmZEXLdwS4HQskcYmmah1HXiyh9mQrN1OyNBZMIOK1wd3BJyHKoJXZXe2ywmY3kVgrUoi9VqOpL2uydnKqcs8oze/KTz4sxY7UduTqf3ROlL1OhXG78GK1TwRS3cKoDtcSuwSBhfcBBbqVALfpShaq+pM3emWgGAHhmT0MM8o5cRqWMBZNcr6cxBrscnEsYBsTN3lS6DIvRgP5Om9JDIUdnZ6eQ667x2WEySLVieL0iSl+mgsHQWOqLpnKIpvM8s1cHaordoYADE9E0soWi0kORDTXpSxGq+pI2eyenEljnt8PE3e5lJ5sV087AbDRgnd+u+6dxUfoyFRpdxq0uBfLmjJVRU+wOdjlRKgNnwmmlhyIbatKXIlT1Je2CKjU2vOwignRaXPIc7HKSbIbaCCL1ZRo3e6fmHj64JGRl1BS7l8pC6OQTNelLEar6kjV7+WIJE7EsP4lrkMEuB86EKw1sGUYNjAVTMBslLgnRGAOBSv4/pfOVAoYha/aq3e55Zk8MTqc4XYcCTuSKJUxEaT5h1YNIfZnGa/bGgkms8zu4JKQO1BS7rg4zul1WUmUhatKXIlT1JZu5ql/uIT7HUgjNNKWtl2qrHD0v5YrUl2mcykk8vEpQD2qL3cEuWu1X1KYvNajqS9bsnQpyQbVIYrGYsGtX66IoPY03ikh9mcaaKheKJZyNpPnBsU7UFruDXU6MBVNN9VZUI2rTlxpU9SVr9saCSfgdZu52r0E6HRZ02s1cZ8OognPRWeSLZZ7Z0yiDAQdis3lEUjmlh8IwikHW7J0KpjDgtys9DLJYrVah1x/S+Y5c0frqnUYOOx+rrRLwzF49qC12q3Xb1bONtY7a9KUGVX3Jmr2xYBIbV7mVHgZZXC6X0OvrvfO9aH31TmNmb67+l0tC6kJtsUut/Yra9KUGVX1Jmr3IXLf7Hnv9CZ1pjFAoJPT6g11OhJJZxGZpnlO4EqL11TuN1OyNhZLwOSzw2i0CR0QHtcVuf6cdFqOBzMOj2vSlBlV9SZq96hPcOl+HwiNhmqW29ELkaZzRLqeCKa7X0zBGg4R1fjvXADO6hqTZq9Z6cc2eOBrtU9Yol5Ze9JmgRevL1M9YMMm7+htAjbFLqf2KGvWlBFV9Sf6rxoIpWEwGbB9YrfRQyOL3+4Vef63PDpNBIpOgG0W0vnqn3l5asdk8Qskcb85oADXG7mCXE2fDaeSL9S/fqxU16ksJqvqSNHungkkM+B2Ix2aUHgpZotGo0OubjQas9dtxalqfM3ui9dU79dbsVcsI+CSe+lFj7A4GHCiUyjgX0f6pPGrUlxJU9SVp9saCKQx1O1AoFJQeClnaoe1gwKnbmT2OXbHU22C3WkbAy7j1o8bYHeyi06hdjfpSgqq+5MxerlDCmUi6dgoDo12Guhw4HaqccczQ5pFHHsGmTZswPDyML33pS1f8/m/+5m+wdetWXH311bj11ltx5syZ2u+MRiN27tyJnTt34uDBg7KOayyUhMkgYa2P63+1TLVtjl4fHhnGpPQA5OZspGIOhrod8Pl8Sg+HLO3QdrDLgVyxhIloGuv8+ppZ0VPsFotF3HXXXXj88cfR39+PPXv24ODBg9i6dWvtNbt27cJLL70Eu92Or33ta/jTP/1TPPDAAwAAm82Gw4cPN/SZ9RZhjwVTWOuzw2wk91wsDDXGrtdugc9hITGzp0Z9KUFVX3IZrHYmbsCJ2dlZhUdDl3ZoO0Ro6aVR9BS7hw4dwvDwMAYHB2GxWHDHHXfgwQcfXPCaffv2wW6vzK7t3bsXExMTLX1mI8u4vITbGGqN3cEAjUbtatWXClT1JWf25tfYUL1paqAd2lbrbPR4bJqeYvf8+fNYs2ZN7e/9/f04f/78kq//1re+hXe84x21v2cyGezevRt79+7Fj3/847o+sx6zVyyVMR5O8U7cBlFr7FJpv6JWfalAVd+GlnHT6TRSqYqZcjgcMJlMiMViAACLxQK3213rPi1JEgKBAGZmZpDPV05B6OzsRDabRTpd2RHldDphNBpr17BarXC5XLVrGAwG+P1+RKPRWtGkz+fD7Oxs7Ya4XC5IkoR4PA4AGLkQRZfLikxiBpFIBEajET6fD5FIBMViEUBla3UqlUImkwEAuN1ulMtlJBIJAJVlIZvNhkgkUhHJZEJnZyfC4XBtF18gEEAikUA2mwUAeDweFItFJJOVZGK322G1Wms7e8xmM7xeL0KhUO1/NIFAAPF4HLlcrnaNQqGwQGOz2YyZmZmaxh6Pp3aNqsaxWKx2Da/Xi3w+L/w+lctlBIPBpu9TR0cHHA4HwuEwACx5nzw2E944F0Iw6NLVfUokEggEAop/n+q9T618n+LxOMrlMuLxOLLZLOLxOIrFImZnZ6+4T//4j/+I559/Hg8//DCASrf7V155Bb29vYjH49i3bx9Wr16NgYGBK+7TAw88gPvuuw8mkwnRaBSxWGzZ+zQxk0GuUEK/21yLdbXmvXbcp3q/T8VisaaXWr5PFosFAwEHQskcxs5Nwm0za/Y+pdNpFItFknlPDd+nSCSiqu/TSvepq6sL9SDVu5wxh+or5d/3tWdhNEh44BPXI5PJoKODT9EQQbu0/W9fexamufupJ/QUu8899xw+//nP49FHHwUA3HPPPQCAz372swte98QTT+CTn/wkfv7zn6O7u3vRa33kIx/B7bffjve9731Lfp4kSdi8eTOOHTu27Lh+NjKN377vRfzgE9fjLQM063hEoNbYfeyNi/j4P7+M//i9G7BrbafSw2katepLBQ3qW9e5sOSWccdDl2psGjnsnGmMdmk7GHBgLKT9OptG0VPs7tmzB6OjoxgfH0cul8P9999/xa7aV199FZ/4xCfw0EMPLTB60Wi09lQcCoXwzDPPLNjY0QrVkpAhrtlrCLXGLpX2K2rVlwpU9SVl9mLpPMKpHAbmzrGsTp0z8tMubQe7nAgmsohn8m35PLWgp9g1mUy49957sX//fmzZsgUf+MAHsG3bNtx999146KGHAAB/8id/gmQyife///0LWqwcO3YMu3fvxo4dO7Bv3z585jOfqcvs1dNUeSyYhMdmhs9hae0fqDPUGrtrfXYYDRLGNf7wqFZ9qUBVX1KtV8bDlS/xAPfYI8PQvDNyd67xKjwaRhQHDhzAgQMHFvzsC1/4Qu3PTzzxxKLvu+GGG3DkyBEhY6ruxKX6pK83LCYD1vrsJDZpMEyjkJrZG5/7Eldn9jS27q4p2qVtdUl+XGcJmmNXLPUYuLFQkpuzN4GaY5dC+xU160sBqvrSMnvBFAwSat3uHQ6utRFFu7Rd47PDIFXurZ7g2BXLSmYvkcljKp7lHntNoObYHexyYDyUQknDp/KoWV8KUNWXlNkbC6WwxmeHxVT5Z1W3TTPy0y5trSYj+jvtutukwbErlpVq9qp1Xbw5o3HUHLuDXU5kCyWcn9FuLzU160sBqvqSMnvjoVRtCZehw0DAofmiakZbXGrOzsu4lBgMVM/I5XzC6AsyZq9cLl9h9oxGo4Ijok07ta2avQZ7Qmoajl1lGQtVSkLW+e1KD0VzqDl2L7Vf0W4NsJr1pQBVfcmYvWAii3SuWHtyA+geaKwG2qntYJcD6VwR04ls2z5TaTh2xbJSQh8PpdDXaYPVRDPxi0TNsRtwWuDqMGl6k4aa9aUAVX3JmL3qtPz8tivVY0oY+WmnttXZWi0n6Ebh2BVL9cijpRgPJbmFU5OoOXYlScJgl1PT7VfUrC8FqOpLxuxVa7oG5hVUr5TQmeZpp7ZVs6enuj2OXeUol8sYD6YWrBIw9aP22B3SePsVteurdajqS8rsWU0G9Lpp9sjRM6s9NlhMBt312mOUIZjIIpUr8mYvogx2OTAZyyCVLSg9FIZpG2TM3lgwhfV+BwyGS/2z/H6/giOiTTu1NRgkDPj1tSOXY1csBsPSqa+2SsBmrynUHrvV5fnTYW3mE7Xrq3Wo6kvG7FVqbBYm51RKm19mLdBubQcCDl21S+DYFctyO7vZ7LWG2mNX62UhatdX61DVl4TZKxRLOBtJL6jXA4BMJqPQiOjTbm0Huhw4G06jUFz5AHsKcOyKZSWzZzEasNpra+OI6KD22F0fqLTTOa1Rs6d2fbUOVX1JmL3zM7PIF8v8JE6YgYADhVIZE1Htdr5ntMFYKIV1fjuMhpXPz2W0h91iQq+nQ1crBQxDwuxVv7SX755zu91KDEcXtFvb6r0d12idTaNw7IplpZo9fnBsHi3E7noN1wBrQV8tQ1VfEmZvPLh4jY2eTlxoN+3WtlZno+GWCY3AsasMxVIZZ8NXloQw9aOF2B3o0q7Z04K+WoaqvjTMXigFd4cJPodlwc8TiYRCI6JPu7X1OSxwd5g0m6AbhWNXLKXS4rWfF2ZmkSuWuMdeC2ghdgcDDsyk84imckoPpWG0oK+WoaovGbM30OWEJHGNDVUkScJAl1M3Zo9RhmpJyHo/mz3KDOisLIRhyJi9xZ7EbTbeTScKJbQdDGh36aVROHbFstSD4Xiw0ribl3GbRwuxu17DZSFa0FfLUNVX82Yvky/i/MzsogXVVG+aGlBC24GAA+dnZpHJ0zzOZj4cu2JZ0uyFUnBaTehyWts8IjpoIXbXdFZ2W2vx4VEL+moZqvpq3uxVu6AvZvaoHmisBpTQtnqPtdr5vhE4dsWyVM3eeDiNgYCDS0JaQAuxazEZsKbTpsllXC3oq2Wo6qt5s7fUTlyGHnrbkcu0n8VO4mFoMhBwcC5hdIPmzd7YMkcbmUymdg9HNyihbfUe66EZKseuWBabucsWipiIztbquZjm0Ersrp+rAdZaqw2t6KtVqOqrebM3Hkqhx22Fw3rlDers7FRgRPpACW0dVhN63FZN1tk0CseuWBZrqnw2nEa5fGVzdqYxtBK7gwEHZvNFTMWzSg+lIbSir1ahqi8Js7fUsks4HG7zaPSDUtoO6GRHLseuWIrFKzf5LLdKwNSPVmJ3IOAEAIyFkgqPpDG0oq9WoaovEbPnXPR3SxVhM62jlLYDAX302uPYbT+nqz322Oy1hFZit9pe53QorfBIGkMr+moVqvpq2uzF0nlEUjledtERgwEHIqkcZtLa63zPqJvxUAoBpwUem1npoTBtoNfdAavJgHGNzewxTDNo2uxVt80v9SQeCATaORxdoZS2tR25xGf3OHbFsljN3tgyJSFM/Wgldg0GCev92isL0Yq+WoWqvto2e3NPZEslaKpn3KkBpbStLr1oLUE3CseuWBbbgTkeSvExaTKgpdgdCDg0t7tfS/pqEar6atvsBVMwSMBan33R32ez2tplpSWU0lbLne8bgWNXLJebvUQmj2Aiy8ekyYCWYnegy4FzkTQKRe3UaWlJXy1CVV9Nm72xUAprfHZYTJr+ZzANUO18r7WncUbdnAlXivS5/ldfDPgdyBfLOD8zq/RQGEYomnZJy7VdAQCPx9PG0egLJbXVQ+d7jl2xXF6zd6ntyuI7+5n60VLsVmdytfTwqCV9tQhVfTVr9srl8opmb7FeWow8KKlttf2K1jrfNwLHbnsZD6YgScA6/+IlIUz9aCl2tXgEo5b01SJU9dWs2ZtOZJHOFZdddkkmeUu9KJTUdqBLm53vG4FjVyyX99IaDyWx2mNDh9mo0IjooKXY9TsscHWYcDqsHbOnJX21CFV9NWv2xoK87KJXBmtn5NL8UjLtZ6VVAoYmkiTp5lQeRt9o1uxVv5zL7Z6z23lJRhRKaquHXnscu2KRJKn253K5zD32ZERrsTsQcNQmD7SA1vTVGlT11bDZS8JqMqDX3bHka6xWaxtHpC+U1HaVuwMdZoOm6mwahWNXLPPNXiSVQyJTYLMnE1qL3YGAAxdis8jktVGrpTV9tQZVfTVs9ipP4gaDtORrotFoG0ekL5TUVqud7xuBY1cs82v26lklYOpHa7E7EHCgXAbORrRxRq7W9NUaVPXVrNnjZRd9M9hF2+wx7aPWdoVPz9Al1f+PaGkpl2EaRZNmr1As4Ww4vaLZM5v5QHNRKK3tQMCBs5E08hrqfN8ISutLnfnLuOOhFEwGCf2dNgVHRAetxe56jdUAa01frUFVX02avYnoLAql8opmz+v1tmlE+kNpbQcCThRKZUxEaXa+V1pf6sxvqjweTGGt3w6TUZPpUHVoLXbdHWYEnNbaWetqR2v6ag2q+moyu1WfwAZXqLEJhULtGI4uUVrbSztytZGgG0Vpfakzv3Hq6XCKj0mTES3G7mDAgdMhbdTsaVFfLUFVX02avXqPNqJ8woLSKK3tIPE6G6X11Qul0son8TCNocXYXR+wa+bINC3qqyWo6qtJszceSsJjM6PTTnNtnVmZTocFXrtZM3U2jDqZjGeQLZRqdVuMPhkIOBFKZhHP5JUeCsMIQaNmr/IkPr/IejECgUCbRqQ/1KAt5c73atCXMtWavfHaSTxs9uRCi7Fbvf+nNZBPtKivlqCqrzbNXrC+ZZd4PN6G0egTNWhL2eypQV/KVJdqqjWfg3zsomxoMXar9d9ayCda1FdLUNVXc2ZvNlfEhVimLrOXy+XaMCJ9ogZtBwMOTMYySOcKSg9FdtSgL2Uumb00bGYjetw0u+YrgRZjd63PDknShtnTor5agqq+mjN7ZyK87MJUqG7Q0couOkZ9jIeSdZWEMLTpMBux2mPThNn7/9l78zi5zurO+1d7d61d3dWbWpJ7U2u1JbwgGxy8CNlAMjKLY8BOEIn9vm8GkgkGByvAZFiGQTiBN8TwZshAQEnmxU7IZyzHAYMk4zEY27KxZVuLJfUmqVut7tq6urqqu9Y7f5RuyXJvtd26z3Pu+X4++QR3V906+t1TT5/7nPOcwzCVIF2wV06Njc/n09ocwyKCtj2SNUMtBxH0pUyxZi+U4DFpNUZW35VlKo+s+soCVX2lC/YutV1ZeYHOZuml90RBBG27A04ANHvtiaAvZRRFQTqbx7noHI9JqzGy+q5aAyx66w1Z9ZUFqvpKF+yNhBJo9zrgclhXfG0iIf5TmqyIoK3TbkWnr0Ga/ljlIIK+lFEUBeeiSeRKmMTDlIesvtsTcCE+n0U4IXbNlqz6ygJVfaUM9nhxZlQon8hltEVts8FpXAaQb0Yuw5SDpMFeaW0SXC5exLVCFG2pBnui6EsVk8l0aewiPzzWFFl9V/WDEcGn8siqryxQ1VeqYG86mUYkkS55cbbZeMKGVoiibU/AhelkBlHBUy/lIoq+VDGZTBgOJeB32tDktOttDilk9d2upkbYLCaMhMUO9mTVVxao6itVsDdSxuEMAJientbSHEMjirbFZqiCL9DlIoq+VMnn8xgJJnhMmgbI6rtWixlrm53C7+zJqq8sUNVXzmCPa2yYi6gpfdEXaEY8uP6XeStUy0IYRrpgz2I2YY3fWdLr7XZOz2iFKNqu9jfCajZhmFj7FVH0pYoCEy7MzHO9ngbI7Ls9ARdGwwnk8+K2X5FZXxmgqq9Uwd5wKIE1/kbYraWZTbU5ogiIoq3tYuqF2hQNUfSlSk4pTMwo9bAXUzoy+25PwI1UNo+JmXm9TVkSmfWVAar6ShXsjQTLS7uEQiENrTE2ImnbE3CR67Unkr4USWVzAHjsohbI7Ls9EpzIlVlfGaCqrzTBnqIoZbVdUd/DaINI2nYHXBgNiZ16KReR9KVIJl/4/+oUFqZ2yOy7l0YwilsWIrO+MkBVX2mCvcmZFOYyubIOZ/Bwc+0QSduegAtzmRwm4+KmXspFJH0pks0DHd4GOO0rT+JhykNm3233OtBoswidKZBZXxmgqq80wZ5agF9OQXUgENDKHMMjkra9BDvfi6QvRTJ5TuFqhcy+azKZhD+RK7O+MkBVX2mCPfXLV05frFgsppU5hkckbdXdXpEX6HIRSV+KZPIKt3DSCNl9t6fVVRylJyKy6ys6ouirKEpNU8ryBHvBBBxWMzq9DSW/J52mNVVBJETStt3TgAabWeii6nIRSV9qmBs8yCs8Jk0rZPfdnhYXzkXnkM7m9TZlUWTXV3RE0ff9d34EH/jdD2N+vjblSfIEexcboJrNNPPpTOWYzSZ0t4idemHEwdq8CgDQ3cLBHrOQnoALubyCc1Fa7ZwYuRgeHcGBZ3+D62+8CVNTU1VfT7pgrxyampo0soYRTdveVhepkWmi6UsJm78LAE/i0QrZfbdYFiJopkB2fUVHJH09t/7fGGvsw9ZrrsPx48erupYUwV42l8fZSLLsYC+TyWhkESOatj0BF86Gk8jmxEy9lIto+lLCdnFnr9RJPEx5yO67anp/VNCHR9n1FR2h9DWZ4X7nPchu/RCuf+dv4cCBAxVfSopgbyw6h2xeKTvYSyTE/LJSQDRtu1tcyOYVjEXn9DalJoimr9Y8+eSTWL9+Pfr7+7F3794Fv0+lUvjwhz+M/v5+bN++HaOjo8Xffe1rX0N/fz/Wr1+Pn/3sZyt+ltW/ClYzSp7Ew5SH7L7b5LSjyWkTtv2K7PqKjoj6urbsgPN9n8UH7voo/vt3v1vRNaRY7dRarF5OuzBL0EvwRK5RyOVy+OQnP4mf/vSnOH78OH70ox8tSFl8//vfh9/vx+DgIO6//348+OCDAIDjx4/jkUcewbFjx/Dkk0/iE5/4BHK53LKfZ2vugk2KlY/Ri56AS9g0LmNMGtZsge93/xs++1++ij/99GeQz5eXxZJiyVOfsMqdY+lycXCoFaJpq/oGlWBPNH215PDhw+jv70dvby/sdjs+8pGPYP/+/Ze9Zv/+/di9ezcA4M4778ShQ4egKAr279+Pj3zkI3A4HOjp6UF/fz8OHz685GcpigKrvwt2PuilGRR8V+ReexT0FZly9K33tA1bcxd8H34I//j4IbzvP7y/rF1IUznGmkwmXeaINO/8j3BufBfG/uajZb3PZrOJlX8nhIjarv5PP0LyxP9G5MB/19uUqhFRXwpY3C1Y/cl9GHv4HuSSYvTTogYF3/XecBf87/oYzn7zQ1AyKb3NuQwK+opMOfq2/PanYW3qwOT//KwmtrTd+UU09l274OdKLoP4T/9fbGl34JlDPy/pybWsYK+rq0tJpQqOr77vzaNFFEUp+79LuUbW7EDeZIYjP1/WNdLpNOx2e83sqPc1Kr1mLa6x0r9tfn4eDQ0NVV2j1honLB6YoMCZm634GpXYocV9SqfTsNlsuvtgPa6RSqWQyWTgdrsv+2+Xy1V8zfT0NDweDywWCwAgGo3C6/Vifn4eVqsVDocDiqIgkUjAZrMV//vNdszPzyOVA8xNnchMDaPZ7xfm+0TpGtWuDSKsexmTHfNWF5yZGCzIV3SNWtix2DVSqRTsdrvudohwn7S4RjlxQ9LqLf7NqbUdsVgMZl87YGss+qACE/IwwZydRz4eRCAQwPnx8Z8pivIerEBZwR4AqSYEb9u2DUeOHNHbDJKIqO39jx7B4ZEInt1zq96mVI2I+mrFc889hy9+8YvFwxVf+9rXAAB//ud/XnzN7bffji9+8Yu44YYbkM1m0dHRgWAwWDzMob72za9bjP//hbP43P96HRf+5i7MJ8VM08kOBd89dj6G3/6bX+E7d1+N376qU29zLoOCviJTqr6KomDrl36OO7Z14Svv31JzO6685u0IbvxdNKzedNnPk0MvInngYXz/u3+LD3/4LgAwLXqBtyBFzV6lvDliZmqLiNr2BFw4H5vDfGb5An0ZEFFfrbjuuutw+vRpjIyMIJ1O45FHHsGuXbsue82uXbuwb98+AMCPf/xj3HrrrTCZTNi1axceeeQRpFIpjIyM4PTp03j729++5GeNhGaRz6SAMoubmdKh4Ltqw+2R0OwKr6w/FPQVmVL1jSTSmJnP1m3GtqIoSLz8b8g+/bc4+OS/q4FeyVg1sksI1JQPU3tE1LYn4IKiAGfCSazv8OhtTlWIqK9WWK1WfPvb38btt9+OXC6HP/zDP8TmzZvxF3/xF7j22muxa9cu3Hvvvfj93/999Pf3o7m5GY888ggAYPPmzbjrrruwadMmWK1WfOc731lWu5FQAtnoefDfS+2g4LsuhxUd3gaMhMSbokFBX5EpVd+R4sFR7YM9JZ9D4unvwTt9Gk+9+AK6u7vLvgbpYO+jHy3vQAdTOiJqq37pRkKz0gd7IuqrJe973/vwvve977KfffnLXy7+74aGBvzLv/zLou/9/Oc/j89//vMlfc5wKIFM9Dx8Pl/lxjLLQsV3uwNOIXf2qOgrKqXqO1ynYC+fSmL2p3+FzZ0ePHHg+YrXLtJp3I997GN6m0AWEbXtvvilE7UZajmIqK/sZHP5wpSVCAd7WkLFd3sCbiHbr1DRV1RK1Xc0lIDVbMJqf6NmtuRmphD75z2448at+MWBJ6tat0gHe1zboB0iaut2WNHmcWBUwAW6XETUV3bGpwuTeDLRcb1NIQ0V3+0NuBBNZjCdTOttymVQ0VdUStV3JJTA2hYnrBbtwqjokw/jC/d/An//P74Lq7W6RCzpYC8QCODhhx/Ghg0bsHnzZnz2s9r0wjEigUCg+L+/8Y1vwGQyIRQK6WhRAZGboZZDIBDAn/3Zn2HDhg246qqr8IEPfADT09N6myU16o5vNjKOkZGRRceyMZVz7tw53HLLLXjXu96FzZs341vf+pbeJlXFpbIQcdaTXC6HnTt34nd+53f0NoUc09PTuPPOO3HjjTdi48aNeO6555Z9/UgogZ4W7VK4/+mTn8C//suj+LMHPlOTAJ90sPdv//Zv2L9/P1599VUcO3YMDzzwgN4mkUENPM6dO4ef//znWLt2rc4WFaAS7E1PT2Pnzp04evQoXnvtMNMiIQAAIABJREFUNQwMDBRbkjCVMTQVBwBkIuPo6elZdCwbUzlWqxXf+MY38Otf/xrPP/88vvOd70itb7eAwd63vvUt9PX16W0GSf70T/8U73nPe/D888/j1VdfxcaNG5d8bT6vFII9Dev1/q8//DjuuOOOml2PdLD3ve99D3v27IHD4QAAtLW16WwRHdQO4/fffz8eeughYVILPQEXQrNpxObk7jCfyWRw2223Fbfur7/+eoyNjelsldy8cGwE5mwK+bkZAFh0LBtTOZ2dnbj66quRyWTg8XiwceNGjI/LmzJf2+yE2SROsDc2NoZ///d/x9133623KeSIxWJ45plncO+99yKTycBut6OpqWnJ11+YmUcqm0dPqzyj60gHe0NDQ/jlL3+J7du346abbsKLL76ot0mk2L9/P7q6urB161a9TSmiPmlRqNt7M3//93+P9773vXqbITWj4STcyiW/WL16tdTBiMiMjo7ilVdewfbt2/U2pWLsVjPWNDuFOfD1qU99Cg899BDMZtJ/tnVhZGQEra2t+IM/+APceuutuO+++5adO1vPtiu1QvrWK+9+97tx4cKFBT//6le/CgCIRCJ4/vnn8eKLL+Kuu+7C8PCwMLtQMrCUvl/+8pfx9a9/HT//+c91sGppei8+aY2GE9i6ZuknM1FYTt8PfvCDAAq+bLVacc8999TbPFIEU6biKD3+g6kdNpsNH/rQh/DXf/3X8Hq9eptTFT0BlxAPjk888QTa2tpwzTXXcO2uBmSzWbz88st4+OGHcc011+Azn/kM9u7di6985SuLvl59AOgNuOtpZlVIH+wdPHhwyd99+9vfxgc/+EGYTCa8/e1vh9lsRigUQmtrax0tlJul9H3hhRcwMjJS3NUbGxvD1VdfjcOHD6Ojo6OeJl7Gmoupl+Gg/gt0KSylr/pU+cMf/hBPPPEEDh06xA8pVTCfyWE6bUZL9DyAQjf6sbExdHV16WwZLTKZDO68807cc889xYcVmeluceHwSGTBDNN68+yzz+Lxxx/HT37yE8zNzSEej+P3fu/38E//9E+62USJ1atXY/Xq1di+fTsSiQTuvPPOZQ9wjQQTaLRZ0O511NHK6iD9eHvbbbfhF7/4BQDg1KlTSKfTl50iZSqnt7cXU1NTGB0dxejoKFavXo2XX35Z10APABxWC7r8jcLU2VRKMpnEk08+iYceegiPP/44nE6n3iZJzZlwYRJCZPQEACCfzy86lo2pHEVRcO+996Kvrw+f/vSn9TanJvS2upBM5zA5k9LVjq997WsYGxvD6Ogovvvd7+LWW2/lQK+GdHR0YM2aNTh58iSSySQOHTqETZs2Lfn6kdAsugMuqR7ASQd7d999N4aHh7FlyxZ85CMfwb59+6S6OUxliNoMtVz++I//GPF4HDt37sS2bdvwR3/0R3qbJC3qJIQHP/EHAAo1ZXfddRc2b96sp1mkePbZZ/GP//iP+OUvf4lt27Zh27Zt+MlPfqK3WVUhYvsVRhsefvhh3HPPPbjppptw5MgRfO5zn1vytaPhJHolqtcDCKRxl6O5uZmffjTC7b68VmF0dFQfQxahN+DCv56J6p56qQa3243BwUG9zSCDWmPze++/Hf8RQF9fX8kj1pjSuPHGG6EoCubm5tDYqN1UgXry5mDvhr4Wna0pcPvtt+P973+/3maQY9u2bXjppZdW9N9MLo+zkSR++8rOOlpXPaR39nhgtHaIrG1PwIV4KovQrFid78tBZH1lZCSYQJvHAbeD9POtEFDy3VW+RtitZqFm5FLSV0RW0vdcJIlcXin2YZQF0sFeLBbT2wSyiKytiM1Qy0VkfWXkrQ1Q8/m8jtbQhpLvms0m9LSI1aidkr4ispK+MrZdAYgHe4wx6S0Ge+I8jTP6MhJKFNvyMEw59ARcwvTaY/RnpNh2Ra71hHSwp07OYGqPyNquamqE3WLGSCiptykVI7K+shFLZhBOpNH9pjmWstZyygA13+1pdeFsOIlsTozdYGr6isZK+o6EEmhy2uB32etkUW0gHex5PB69TSCLyNpazCZc0eKUemdPZH1lYyS8MO3CwZ52UPPdnoAL2byCseic3qYAoKevaKykr9YzcbWCdLAXCoX0NoEsomvbHRCrzqZcRNdXJtSg/81pXK7Z0w5qvtsrWA0wNX1FYyV9R0IJ9LRwsMcwQtAbcGE0nEQ+r+htCqMzI8EEzKbCdBWGKRfutceozKVzmIjN886eaPD8S+0QXduegAvpbB7nY2KkXspFdH1lYiScxGq/Ew4rt6yoB9R8t9llh7fBKkywR01f0VhO31G1JETCw16kvaalRYwmmBQRXVvZn8ZF11cmRkKzC57EuVeZdlDzXZPJhJ5WcabyUNNXNJbTV9a2KwDxYC8ajeptAllE11b2YE90fWVBURSMBBcWVHPNnnZQ9N1egWqAKeorEsvpq/pAN9fsiUU2m9XbBLKIrm2rxwGX3YLhoBgLdLmIrq8sBOMpJNK5BT32FIVrObWCou/2BFwYn57DfCantykk9RWJ5fQdDibQ7nXAJeEkHtLBHmNcCqkXV7HGgjEmwxKnXRhxUP2H1xNjMxqWs+0KQDzYa25u1tsEssigbU9AnDqbcpFBXxlYKu3CRe7aQdF3i2UhAmQKKOorEsvpW+ix566jNbWD9Io3NyfnSUwZkEHbnhYnzkWSSGflq8+SQV8ZGAklYLeasaqp8bKfcxpXOyj6rhrsiTA2jaK+IrGUvtPJNCKJtHRj0lQ42GMqQgZte1pdyCvA2Yh8Y9Nk0FcGhoMJdLc4YTFfPjGDgz3toOi7LocV7V6HEJkCivqKxFL6FrMEHOwxjFio2+2jAizQjD4s1naFYSqhR6ATuUz9kbntCkA82OMZgtohg7bqSBsZF2gZ9BWdXF7B2Uhy0RobrtnTDqq+K0oNMFV9RWEpfUdDhUk8ayWdxEN6xeNh59ohg7Y+pw3NLrsQdTblIoO+ojMenUMmp0hbYyMrVH23N+BCJJHGdDKtqx1U9RWFpfQdDiWwptkJu1XOsElOq0tkZmZGbxPIIou2hdTLrN5mlI0s+orM8MX7vthoI26qrB1UfVeURu1U9RWFpfQdCSWkbKasQjrYY5iegAujIfkOaDDVI3uNDSMW6kOD3sEeU38URbnYdkXetYR0sNfQ0KC3CWSRRduegAsXZuaRSMnVdV4WfUVmJJSAp8GKFpd9we84FaYdVH13jb9wqlvvYI+qvqKwmL7BeArJRSbxyATpYM/lkvfGiI4s2sra+V4WfUVGfRJfLLDjYE87qPqu3WrGGn+j7jXAVPUVhcX0pTCJh3SwFw6H9TaBLLJoK0qdTbnIoq/IDAeXTrtwzZ52UPbdnoBL9ykalPUVgcX0pVASQjrYYxi1oFbvBZqpL/OZHM7H5qRenBnxUNuvcFNuY1GcxONrXPnFgkI62LNYLHqbQBZZtG20W7DK14ARydK4sugrKmfCSSiK3E/iskLZd3taXZjL5DA5k9LNBsr6isBi+qqTeMxmecs/SAd7PDBaO2TStlvCzvcy6Ssiarud3iWGlvMfTO2g7Lu9xRm5+rVzoqyvCCym72hY7pO4APFgLxKJ6G0CWWTSVsYxRzLpKyIjF9vtdAcW73afy+XqaY6hoOy7ItQAU9ZXBN6qby6v4Ew4segkHpkgHezxgq4dMmnbE3BhOplBNKFv5/tykElfERkJzaLV44Cnwaa3KYaDsu92eBvgsJp1rQGmrK8IvFVfdRJPzxIPjrJAOthjGADF3kiy1e0xlSN7A1RGTMxmk5SZAqZyipN4eGdPXFpaWvQ2gSwyaSvjiVyZ9BWRkVBi2Zm4ZjPppU9XqPuu3sEedX315q36jhJouwIQD/YSCXn+uMuGTNquaRaj8305yKSvaMTmMgjNppddnLl1hnZQ992egAtnI0lkcvr0aqSur968Vd+RUAIehxUB98JJPDJBOtibn5/X2wSyyKStzWLG2manVMGeTPqKhvok3s3Bni5Q992egAvZvIKx6Jwun09dX715q77DoQR6WhefxCMTpIM9hlHRO/XC1A/1Pi+XxmWYSinWAOvYfoWpHyOhRLEUSGZIB3ter1dvE8gim7bdLS6pOt/Lpq9IDIcSMJmAtS1Ln57jmj3toO67aqH+sE41wNT11Zs36zufyWF8msYkHtIrnix/2GVENm1F6HxfDrLpKxIjoQRW+xvhsHLjZD2g7rt+pw2+RptumQLq+urNm/U9FylM4lF3c2WGdLAXj8f1NoEssmkrQuf7cpBNX5EYDa3cADWf16e43ghQ912TSd/2K9T11Zs36ztM5CQuQDzYYxgV9cs6enGyAkMTRVFWbLvCMNXSyzXAhmCkhMNeskA62GtsbNTbBLLIpm2HtwENNrM0RdWy6SsKwdkUZlPZFZ/EZT9ZJzJG8N2egAsTsXkk09m6f7YR9NWTN+s7Ekwg4LbDS2ASDwd7TEXIpq3ZbCoe0pAB2fQVBbVxNgd7+mEE3+1p1S9TYAR99eSyYI/QJB7SwR4PjNYOGbXtCbiKNRiiI6O+IjBSYo0N1+xphxF8V/UvPR4ejaCvnrxZ35EwB3sMIx09ARfORZLI6tT5ntGekVACdosZq5p494PRjuIIRknKQpjyic9nEIynSNTrAcSDPavVqrcJZJFR256AC5mcgvFpfTrfl4OM+orAcCiBK1oK4/GWg9O42mEE33U5rOjwNuiSKTCCvnqi6qv2Uexd4WS/LJAO9vx+v94mkEVGbXuK7VfET+XKqK8IlFpjw02VtcMovqtX+xWj6KsXqr7qve0j0GMPIB7shcNhvU0gi4zaFutsdOp8Xw4y6qs3ubyCM+FEsXh+2dfmcnWwyJgYxXd7WvUJ9oyir16o+g4HZ2FeYRKPTJAO9rgIWztk1LbZZYe3wSrFiVwZ9dWb8egcMjmFe+zpjFF8tzfgwnQyg2giXdfPNYq+eqHqOxRKYE2zk8wkHtLBHsO8GZPJhJ5WtzRTNJjyGAmrJ3Fp1NgwYiNTWQhTPsNBOidxAeLBXiAQ0NsEssiqbV/ApdsA83KQVV89GQkWgniu2dMXo/jupak89V1PjKKvXgQCAeTzCkZCs2QOZwDEgz2eIagdsmrb26pf5/tykFVfPRkJJeBxWBFw21d8LQ+T1w6j+O6a5sKp73qXhRhFX72Ix+O4MDOP+UwevUQOZwDEg71UKqW3CWSRVdve1sKTmui7e7LqqyfDocLhjFLaqnCwpx1G8V2bxYy1zc66B3tG0VcvUqnUpbYrHOwxjJyoX16us6HHcDBRbHbLMPVApqk8TOmodd19rZzGlQKfz6e3CWSRVdvuFhdMpsKxepGRVV+9mM/kcD42V/KTONfsaYeRfLcn4MJoKIF8vn47xUbSVw98Ph+Ggwm47Ba0eRx6m1MzSK943EtLO2TVtsFmQVdTo/DtV2TVVy9GQgkoyqU0PaMfRvLdnoALc5kcJuPzdftMI+mrB7lcDkPB2ZJLQmSBdLA3Oyv27o3MyKxtb6tb+Jo9mfXVA/V+ltrtnnuVaYeRfLdXh0btRtJXD2ZnZzESSpA6iQsQD/YYZjF6Ay4MB2e5SJ8Qw2W0XWGYWqFOaxkSPFPAlM58Jo/x6dJLQmSBdLDndNIYcyIiMmvb2+pCIp3DVFzcU20y66sHw6EEVvka4LSXNiSeUnpGNIzkux3eBjTaLHXd2TOSvnoQmgcUhd6DI+lgz+GgU1wpGjJrq27PDwl8SENmfcshEolg586dWLduHXbu3IloNLrgNUeOHMENN9yAzZs346qrrsKjjz5a/N3HP/5x9PT04PFfvICpoaM4cuRISZ/LwZ52GMV3gYtTeQKuuq4lRtJXD8ZmMgBoncQFiAd7i/3hYGqDzNoW268IXLcns77lsHfvXuzYsQOnT5/Gjh07sHfv3gWvcTqd+Id/+AccO3YMTz75JD71qU9henq6+PuHHvpLNLZ34+7/sAPbtm0r6XO5Zk87jOK7Kn1t9R3BaDR9682JsTAA3tljGOlRUy8iB3tGYf/+/di9ezcAYPfu3XjssccWvGZgYADr1q0DAKxatQptbW0IBoPF38czwGwqWyyWZ5h60htwYSw6h/kMn5KlwJnIHDq8DXA5SisJkQXSwZ7NZtPbBLLIrK3ZbLrYDFXcNK7M+pbD5OQkOjs7AQAdHR2YnJxc9vWHDx9GOp1GX19f8Wdf+87fAwB+/q//VPJ0AU7jaodRfFelt9UFRQFGw/V5eDSavvXm7HSK3OEMACgrdE0mk0gkCg7tcrlgtVoRi8UAAHa7HV6vF6FQCEBhMQ0EApienkYmU8iB+/1+pFIpJJNJAIDb7YbFYilew+FwwOPxFK9hNpvR0tKCaDSKbLYwy7S5uRlzc3OYm5sDAHg8HphMJszMzAAAGhoa4HK5EA4XtmIjkQiam5sRiUSK/YlaWlqQSCQwP1/ojeT1eqEoSnHmYGNjIxobGxGJRAoiWa3w+/0Ih8PF9E8gEEA8Hi/+cfH5fMjlcsVj8U6nEw6Ho7jlbrPZ0NTUhFAoVDwFGggEMDMzg3Q6XbxGNpu9TGObzVZMWdntdvh8vuI1VI1jsVjxGk1NTchkMprfJ5/PV9xdqcV9slgsdb1PXV4rjl+YQSKREPY+KYoi3Pepkvt01113IRgMXtYfzGKx4MEHH4SiKAgGg8X7BADBYHDR71MkEsHdd9+Nhx9+GDMzM2hqasIDDzyA6y8Aew+eQS56Hl/60pdw//33L3qfHn30UfzgBz+A1WrF9PQ0YrGYMN8nEe5TrdY9t9tdXBtE+j5pdZ/aGgraHBmawLpWV13uUy6X479PGnyfYrEYRsNzeO9mD/L5vBDfp5XuU2trK0rBVGb7Cal6VYRCIQQCAb3NIIns2n7zwCl8+6nTOPGV98BhtehtzgJk17dU1q9fj6effhqdnZ2YmJjAzTffjJMnTy543czMDG6++WZ87nOfw5133nnZ777yxHH8zxfO4P+7tQHf/MY38MQTTyz7mSaTCQMDA4t+DlM9RvFdlWQ6i01/8TM8cNsA/vjWdZp/ntH0rSeh2RSu/a8H8Z9/ZxPuvbFHb3NKpaQ0Bek0LvdR0w7Zte1rdSGvAGfCSb1NWRTZ9S2VXbt2Yd++fQCAffv24Y477ljwmnQ6jQ984AP42Mc+tiDQm5iYwHBwFj0BFx7fvx9btmypi93M0hjFd1Wcdis6fQ0YqlMNsNH0rSfqZCWKaVzSwR7DLIXafkX0GbnU2bNnDw4cOIB169bh4MGD2LNnDwDgpZdewn333QcA+Od//mc888wz+OEPf4ht27Zh27ZtxRYr99xzD55++QRO/+ZXCIVC+MIXvqDbv4UxLn2tbl5LCKDewz5i0zMA4mlctW6AqT2yazubymLLf/kZ/uz29fjkLf16m7MA2fWtF6lsDhv/85P441v68enb1pf0HpPJhA0bNuDEiRMaW2dMjOi7f7H/KP7Xy+N47Yu3af5vN6K+9eJrPzmBHzw7ihNfeQ8sZmk05jSuWhTL1B7ZtXU7rGj3OoRtvyK7vvXiTDiJvFKYd1wOnArTDiP6bm/AhXgqi2AdpvIYUd96MRRMYLW/QaZAr2RIB3vqCSCm9lDQtjdQ32ao5UBB33qgpl3KrbHhYE87jOi7fW3qVB7tHx6NqG+9GA7NYm0TzQklpIM9hlmO3lYXhoMJ/sMvMeof13J39himlqj+J+rDI7My2VweZ8NJXNHcoLcpmkA62PP5fHqbQBYK2va2uhGbyyCSEO9JmYK+9WA4mEC71wF3md3uzWbSS5+uGNF3O70NaLCZMTSl/c6eEfWtB+eic8jmFWzsatbbFE0gveKpDRSZ2kNB2+KM3JB4dXsU9K0Hw6HZ4snqcuDdXO0wou+azaa6lYUYUd96oJaErOE0rnyoXbqZ2kNB2z6B269Q0FdrFEXBcDBRUU8sDva0w6i+q5aFaI1R9dUa9d61NdJcG0gHewyzHF3+RtgtZmFP5DLLE0mkEZvLcL0eIwS9rW6MRZOYz+RWfjEjHMOhBPxOG5oaac4eJh3suVz0umCLAgVtLWYTrmhx1q3zfTlQ0Fdrhqvods99yrTDqL5br6k8RtVXa4aDs+htdZPVl3SwZ7PRjNBFgIq2va0uIU/QUdFXS4amKu92z8GedhjVd/ta61MWYlR9tWY4lEBPwEVWX9LB3vT0tN4mkIWKtr2tbpwNJ5HJ5fU25TKo6Kslw6EE7FYzuvyNZb83nxfrflPCqL7bE6jPgS+j6qsl8fkMgvEUeltdZPUlHewxzEr0BlzI5hWci2ibemFqz3BwFj0tLpLd7hn5cDms6PQ1FHecGXlQ67YrOdkvC6SDPbvdrrcJZKGibbEZqmB1e1T01ZJKT+ICnMbVEiP7bm+rC0Ma7+wZWV+tGLl4z/paXWT1JR3scfNJ7aCibV+x155YT+NU9NWKTC6Ps5FkxcEeN1XWDiP7bm/AjeHgrKatfYysr1YMB2dhNgFrW5xk9SW94oVCIb1NIAsVbZucdjS77MLt7FHRVyvORpLI5pWK0y65HLfH0Aoj+25fqwvx+SyCsynNPsPI+mrFUDCBtc1OOKwWsvqSDva4cap2UNK2N1CfZqjlQElfLSjW2FS4s8doh5F9tx5lIUbWVysGp2aLp6mp6ks62OO6HO2gpK2I7Vco6asFansLbqgsHkb23eIIRg2DPSPrqwW5vIKRUAL9bYW1hKq+pIO9QCCgtwlkoaRtb6sbodnCNAZRoKSvFgwFZxFwO+CrsNu9xWKpsUWMipF9d5WvEQ02M4Y07LVnZH214FwkiXQuX9zZo6ov6WAvFovpbQJZKGmrfsm1XKDLhZK+WlDNSVyA++xpiZF912w2oefiIQ2tMLK+WqCu+31thfWEqr6kg710Oq23CWShpK16Ilek/liU9NWC4VCieN8qgWpdjggY3XcLZSHapXGNrm+tGVQn8Vx86KeqL+lgj2FKYW2zEzaLCYMC7ewxSzOdTCOSSJNugMrIS1+rG+ciSaSyfOJbBgolIXY0OWn211MhHew1NTXpbQJZKGlrtZjR3eLC0JQ4J3Ip6VtrhmpwEpf77GmH0X23r9WFvAKcCWszlcfo+taaoWCiuKsH0NWX9IqXyYhTcE8Natr2t7mFqtmjpm8tqcVJXE7jaofRfVfdcdaqbs/o+tYSRVEKbVfaLq0lVPUlHewlEuLs1FCDmrb9bW6cCSeESb1Q07eWDAZnYbeYscbfWPE1ONjTDqP7rrrjPKRR+xWj61tLwolCF4b+Nz04UtWXdLDHMKXS3+ZGXgFGQ9qkXpjaMTQ1i56AC1YLL1+MeLgcVnR4G4TKFDCLUzyc0Ua//pf0aulycXd9raCmrWjtV6jpW0sGp2aLDVArhWrjVBFg3714IlejnT3Wt3ao6/2b1xOq+pIO9qxWq94mkIWatmrqZVCQ9ivU9K0V85kczkaSVT+Jc7CnHey7hYfH4eCsJuUCrG/tGJpKoNFmQae3ofgzqvqSDvaoNkcUAWraOu1WdDU1ChPsUdO3VoyGE8grqHpnj5sqawf7buFE7sx8FsHZVM2vzfrWjsHgLHpbXTCbLz38UdWXdLDHMOXQJ9iJXGYhajDezzNxGYHpb/MAECdTwCzOUA1KQmSBdLBnt9NukqgnFLXtby0Ee/m8/ic1KepbCwanZmEyVddjD+A0rpaw717aedZiKg/rWxuS6SzGp+cu67EH0NWXdLDn9Xr1NoEsFLXtb3NjPpPH+PSc3qaQ1LcWDE7NYo3fiQabparrcLCnHey7QLvXAbfDitMaBHusb21QD9C8dWePqr6kg71QKKS3CWShqG1xRq4AqVyK+taCWpzEBbhmT0vYdwsPE/1tbk3SuKxvbVDX+bfu7FHVl3SwxzDloAYRXGcjJrm8guFQwjA1NozcaBXsMbVhaGoWZhPQHXDqbUpdIB3scapGOyhq2+J2wO+0CbGzR1HfahmLJpHO5vlwhuCw7xbob3NjKp5CbK6247dY39owFExgbbMTDuvlJSFU9SUd7AUCAb1NIAtVbfvb3Bia0n9cDlV9q6GW3e4tlupq/pilYd8toD6U1Hp3j/WtDUuVhFDVl3SwNz09rbcJZKGqbV+rG4MC7OxR1bcaatl2hWv2tIN9t8C6dm1O5LK+1ZPLKxgJJRbU6wF09SUd7GUytd0+Zy5BVdv+NjciiTQiibSudlDVtxoGp2YRcDvgc9qqvpYWkw2YAuy7BVb7nbBbzTV/eGR9q+dcJIl0Lr9osEdVX9LBHsOUi5oiFKFuj7mcweAs+ttozq1k6GExm9AbcPEhDQEpnsQ10GEv0sGe3+/X2wSyUNVWqzqbcqGqb6UoilKztisAYDaTXvp0hX33Ev1tbpyeitf0mqxv9SxXEkJVX9IrXipV+7mETAGq2nY1NaLBZtY92KOqb6UE4ynE57M1O4nLaVztYN+9xLo2D8aic5jP5Gp2Tda3eoaCS5eEUNWXdLCXTCb1NoEsVLU1m03oDeg/I5eqvpVSfBK/OHO0WjjY0w723Uv0t7mhKLUtC2F9q2comCg20X8rVPUlHewxTCX0cTNU4VCL3LmhMiMT3KhdPGpdEiILpIM9t9tYN7OeUNa2v9WN8ek5zKVrl3opF8r6VsLQ1CzcDivavY6aXI9r9rSDffcS3QEnLGZTTYM91rc6wok0YnOZRU/iAnT1Jb3iceNU7aCsrRapl3KhrG8lDAZn0dfmJtvdnhLsu5dwWC24otlZ02CP9a2OlZqzU9WXdLAXi8X0NoEslLXtF6D9CmV9K2FwaramY9K4qbJ2sO9eTq3LQljf6hhaoSSEqr6kgz2GqYTugBNmU+073zOVMTOfweRMynA1NgwN+tvcGA0nkMnxA4YIDE0l0GizoNPboLcpdYV0sOdw1Ka+h1kIZW0dVgvWNjt1HZtGWd9yGZqq/eEMTgdrB/vu5axrcyOTU3AwOiK3AAAgAElEQVQmXJtTnqxvdRRKQlwwmxdfA6jqSzrY83hq06aBWQh1bfvb3BiaSuj2+dT1LYdBDvakgn33cmp9Ipf1rY6hqdklD2cAdPUlHeyFQiG9TSALdW37Wt0YCSWQ1Sn1Ql3fchgMzsJuMWONv7Fm1+SaPe1g370cNbCoVQ0w61s5yXQW49NzywZ7VPUlHewxTKX0tbmRzuVxLjqntymGZ2hqFj0BF6wWXq4Y+XA5rFjla+BeewKg3oOBduPV/5JePbmXlnZQ17Z4IlenBZq6vuVgxAaoMsO+u5D+dk/NZuSyvpVzanLlSTxU9aX5r7pIS0uL3iaQhbq26ja/Xoc0qOtbKvOZHM5Gkkv2xKoUqr20RIB9dyH9rYUa4Hy++jF9rG/lnJ6Mw24xo7vFueRrqOpLOtiLRqN6m0AW6tr6Gm1o9Th0S71Q17dURsMJ5JXaj0njmj3tYN9dSH+bG3OZHM7Hqi8LYX0r5/TULHpbly8Joaov6WAvm83qbQJZjKDtujY3TusU7BlB31IonsStYUNloDAfk9EG9t2F1PJELutbOacm41jXvvxpW6r6kg72GKYaBto9GJyM1yT1wlTG4NQsTCagt9WltykMUzHratx+hSmfRCqLsegcBgxa/0s62GtubtbbBLIYQduBdg8S6RzGp+t/ItcI+pbC4NQs1vidaLDVtsaOahG2CLDvLsTvsqPFZa9JsMf6Voaq/Uo7e1T1Jb3izc1x2wytMIK26vH8Wp2iKwcj6FsKWp3E5TSudrDvLk6tZuSyvpVxarKwjq/UdoWqvhzsMRVhBG3VJ0D1uH49MYK+K5HN5TEcSnCwJxnsu4vTf7EGuFrfY30r4/TULOxWM65oWb4khKq+pIM9hqkGX6MNHd4GnLpQ/509BjgTSSKdzWNghbQLw8jAujY3YnMZhGbTeptiSE5NxtHX6oZliZm41CEd7FGdcScCRtF2Xbsbp3RI4xpF3+U4fTHtsl6DYI9r9rSDfXdxanUil/WtjNOTsyVNzqCqL+kVj4eda4dRtB1o92Bwaha5Op/INYq+y3HyQuEkLk/PkAv23cUpBntVNmpnfctnNlWYiVtKloCqvqSDvZmZGb1NIItRtB1od2M+k8e5SLKun2sUfZfj1GQca5udaLTXftoFN1XWDvbdxenwNsDtsBZ3rCuF9S0fVfN1JTw4UtWXdLDHMNUyUDykwXV79ebUZJzr9RgymEwmDLS7eS3RgdMXD9kZeT0hHew1NDTobQJZjKKteiK33pM0jKLvUqSyOYyEEprU6wF0UzUiYHTfXY71HR6cvBCv6kQu61s+pybjcFjNWNO89ExcFar6kg72XC7uuq8VRtHW7bCiq6mx7k/jRtF3KUZCCWTzCgY6ONiTDaP77nIMtHsQTVZ3Ipf1LZ9TU7Mln8Slqi/pYC8cDuttAlmMpO1Auxsn69x+xUj6Loaqdymn5yqBa/a0w+i+uxzra1AWwvqWz+BkvOS1hKq+pIM9hqkFA+0eDAcTyOY4QKgXpydnYTWb0Bvgk7gMHdSd6no/PBqZ+HwG52PzK45Jow7pYM9iqf0pPqaAkbRd1+5BOpfHmTqeyDWKvpFIBDt37sS6deuwc+dORKNRAMDJyTh6Ai7YrYUlymKxYNu2bdi2bRt27dpVfP/IyAi2b9+O/v5+fPjDH0Y6zQ1r9cYovlsJAbcDLS57VcEe61sear11qYczqOpLOtijOtBYBIykbTH1UsencaPou3fvXuzYsQOnT5/Gjh07sHfvXgALT+I2NjbiyJEjOHLkCB5//PHizx988EHcf//9GBwchN/vx/e///2SPpfqgi4CRvHdSlnf4cHJKtK4rG95nC5xJq4KVX1JB3uRSERvE8hiJG3729wwmeo7I9co+u7fvx+7d+8GAOzevRuPPfYY5tI5nI0kV3wSVxQFTz31FO68887L3l8KuVyuOsOZJTGK71bKQLsHpyfjyFfYqJ31LY9Tk7NosJmxxr/ySVyArr7Wcl6cTCaRSCQAFE6sWK1WxGIxAIDdbofX60UoFAJQOO0WCAQwPT2NTCYDAPD7/UilUkgmC+kwt9sNi8VSvIbD4YDH4ylew2w2o6WlBdFoFNlsFkAh6p6bmysOK/Z4PDCZTMVGiA0NDXC5XAiHw8Wb1tzcjEgkUlzgW1pakEgkMD8/DwDwer1QFAXxeOEJoLGxEY2NjcX3W61W+P1+hMPhYmF3IBBAPB5HKpUCAPh8PuRyOczOFgICp9MJh8NRTEvZbDY0NTUhFAoVj90HAgHMzMwUU08+nw/ZbPYyjW02G6anp4sa+3y+4jVUjWOxWPEaTU1NyGQymt+nbDaLYDBYk/sEFHZaRL5PXb4GvH42hGCwqS73KR6Pw+/3C/V90uI+XbhwAVarFYqiwOl04sKFC3jx1DkoCtDdbC/62Pz8PK655hqYTCb8yZ/8Ce644w5ks1l4PJ7ivevq6sK5c+eK73nrfXr00Ufxgx/8AFarFdFoFLFYTJjvk+j3qZzvUzqdLt4DauteLe7TQLsbiXQOrw+PY02zs+z7lEwm4fP5+O9Tiffp2FgE/W1uxGLTJd2nSCQCm80mzPdppfvU2tqKUjCV2e+nvjOjqiQYDJYsBFMeRtP2vn0v4Uw4gQOfvqkun0dJ33e/+924cOHCgp9/9atfxe7du4t/LIDCQv4/Dr6OB/7lVRz6zE3oay2kXsbHx9HV1YXh4WHceuutOHToEHw+H66//noMDg4CAM6dO4f3vve9OHr06LL2mEwmDAwM4OTJkzX8VzIqlHxXC35zJooP/e2v8f3d12LHxvay38/6lsf1/+0Q3tHXgm9+eFtJr5dQ35L6SJW1sycbLS0teptAFqNpO9DuxtMnp5DO5ouHBrSEkr4HDx5c8nft7e2YmJhAZ2cnJiYm0NbWhlOTcditZlzxpgaoXV1dAIDe3l7cfPPNeOWVV/ChD30I09OFp3Wr1YqxsbHi61bCbCZdwaIrlHxXC9TasZOT8YqCPda3dGJzGVyYKe8kLlV9Sa946lYxU3uMpu1AuwfZvILRcH3+3UbRd9euXdi3bx8AYN++fbjjjjtw8kIc/a1uWC2F5SkajRbTHKFQCM8++yw2bdoEk8mEW265BT/+8Y8ve38pVDPBgFkeo/hupXgabIVG7RUe+GJ9S2dwqvx+nVT1JR3sqbl0pvYYTVv1sEC9+mMZRd89e/bgwIEDWLduHQ4ePIg9e/bg9GQcLbY07rvvPgDAiRMncO2112Lr1q245ZZbsGfPHmzatAkA8PWvfx3f/OY30d/fj3A4jHvvvbekz+VgTzuM4rvVMNDuxskKD3yxvqVzqoKZuFT1JZ3GZZha0dvqgtl06Rg/UxtaWlpw6NCh4n/PXGyA+ns3rMcnPvE9AMA73vEOvP7664u+v7e3F4cPH66LrQxTKwY6PHh2MIxMLg+bhfSei66cmoyj0WZBV1Oj3qboDmkv83q9eptAFqNp22CzoLvFVbf2K0bTV0UNptdr3O2ea/a0w6i+Ww7r1UbtFZSFsL6lc3pyFuva3TCXMBNXhaq+pFc8TtVohxG1HWj3VDXTshyMqC9QWdqFEQuj+m45rC+OTSv/4ZH1LZ1Tk3GsaytvLaGqL+lgT+13w9QeI2o70O7GaDiB+Yz2DXmNqC9QqIl02rVPu6h9rpjaY1TfLYe+VjfMJlQ0SYP1LY1YMoOpeArryjicAdDVl3SwxzC1ZF27B3kFGA7SPK0lAqcm41jX7ikr7cIwstFgs6A74KrrCEajcbqCk7iUIR3sNTZyUaZWGFFbNfWiLiJaYkR9gUKwt74Oi7PJxMGkVhjVd8tlfYVlIaxvaaglIeWmcanqy8EeUxFG1La7xQWr2VSX9itG1Dc8m0JoNl2Xej0O9rTDiL5bCQPtnorKQljf0jg1WVlJCFV9SQd7VAcai4ARtbVbzegJ1OdErhH1refhDK7Z0w4j+m4lrO8olIUMTpW3nrC+pXHyQhwDFZSEUNWXdLDHMLVmoN1TlzSuEVFTWmq6nGEoU+9G7UZCURS8cWEGGzt5LVEhHexZrdwzWiuMqu1AuwdnI0nMpbU9kWtEfU9OxuFtsKLN49D8sziNqx1G9N1K6G5xwm4xl123x/quTDCeQjSZqahfJ1V9SQd7fr9fbxPIYlRtB9rdUBTtD2kYUd/Tk3Gs7/DUJRDjpsraYUTfrQSrxYy+NnfZ7VdY35U5cXG3dENn+Q2SqepLesULh8N6m0AWo2qrLh5ap16Mpq+iKMUam3qQy2nfK9GoGM13q2FDh6fs9ius78qcvDADoKBvuVDVl3Swx0XY2mFUbdc2O9Fos+ANjYM9o+k7OZPCzHyWJ2cQwGi+Ww0D7R6cj81jZj5T8ntY35V5YyKOdq8DTU572e+lqi/pYI9hao3FbMJAhwcnJmb0NoUUaiqLgz3GSKzvKPSUPF2nMYxG4Y0LcWzooDnjtlJIB3uBQEBvE8hiZG03Xgz2tJyhaDR91VRWvbrdc82edhjNd6vh0onc0tuvsL7Lk8nlMTg1W1EKF6CrL+kVj+qMOxEwsrYbOjyIJjMIxlOafYbR9D1xYQZtHgda3NqfxAXoDjsXAaP5bjV0NTXCZbcUa8xKgfVdntFQAulcHhsqbLtCVV/SwV4qpd0fY6NjZG3XX0wPnNCwbs9o+r4xEa/o5FylcLCnHUbz3WowmQplIeWcyGV9l0ddl9e3V7aeUNWXdLDHMFqgpgfe4Lq9mqCmXbgBKmNE1rd7cPJCnB9AasQbEzOwmk3oa3PpbYpQkA72fD6f3iaQxcja+l12dHgbND2RayR9h4OFtMvGOhZUc82edhjJd2vBQPvFspDZ0naUWN/lOXkhjt5WFxxWS0Xvp6ov6RWPe2lph9G13dCp7YlcI+mr6rixjmlcRjuM5Lu1QPX7ExOlPTyyvstT7UlcqvqSDvZmZ7UfWG9UjK7thg4vhoKzSGe16clkJH1PXJiB3WJGb2v90i5Ue2mJgJF8txZsKgZ7pT08sr5LMzOfwfj0XFXztanqSzrYYxit2NjpQSanYDhEc2GoJycm4uhvc8Nm4eWIMR4+pw2rfA3cu7MGqJONuP53IaRXV6fTqbcJZDG6tmqa4I0SUy/lYiR935iYqbhNQqXUY/6uUTGS79aKjZ3ekoM91ndp1ENz66tI41LVl3Sw53DUp2eXETG6tr2tLtgsJpwooz9WORhF3/BsClPxVDGVVS842NMOo/huLdnY6cVQMIH5zMr1Yqzv0hyfiMPXWNgprRSq+pIO9qLRqN4mkMXo2tosZvS3eTTb2TOKvm8U0y71Dfa4Zk87jOK7tWRjpxe5vILBqZXLQljfpTk+MYNNnd6qHuao6ks62GMYLdnIM3KrRtWv0tFGDEMBtcbs+HleTyoll1dw8sIMn+pfAtLBns1m09sEsrC2hafxqXgK4RL7Y5WDUfQ9MRGv65g0FU7jaodRfLeWXNHigtNuwfESHh5Z38UZCSUwn8lj06rqgj2q+pIO9pqamvQ2gSysLYqLSqn9scrBKPqemJip65g0FW6qrB1G8d1aYjGbsL7ETAHruzhqoFxt/S9VfUmveKFQSG8TyMLaXlpUjk/Ean5tI+ir55g0qo1TRcAIvqsF6onclcamsb6Lc2JiBjaLCf1t7qquQ1Vf0sEezxrUDta2MDZtla8BxzSoszGCvnqMSWO0xwi+qwUbO72Ymc/ifGx+2dexvotz/PwM+ts8sFurC2uo6ks62GMYrdm0ystF1RWi7ohWW2PDMBTYdHGH+wSvJxWhnsRlFod0sBcIBPQ2gSysbYFNnYWxaaX0xyoHI+h7bHwGDqsZvYH6jUlT4Zo97TCC72qB2gh4pbo91nchwXgKwXiqJiUhVPUlveLNzPATklawtgU2rfIir1wa01MrjKDvsfOFwxlWHcakUU3ViIARfFcL3A4rrmhxrtionfVdiBog1yJLQFVf0sFeOp3W2wSysLYFNnX6AKDmdXvU9VUUBcfOx7BZpxQuB3vaQd13tWRT58plIazvQk7U6CQuQFdf0sEew2jNmuZGeBxWTU7kUmYsOoeZ+axuwR7DiMjmVV6MhpOIz2f0NkUqjk/MYJWvAU1Ou96mCAvpYM/n8+ltAllY2wImkwkbNTikQV1fdSdUr4JqrtnTDuq+qyWbVxW0W653J+u7kOPnZ2p20IuqvqRXvGw2q7cJZGFtL7Gp04s3LsSRy9cuNUhd3+PnYzCbgA06tV3hNK52UPddLdncVfg+HB1fOlPA+l7OfCaH4VCiZmPSqOpLOthLJBJ6m0AW1vYSm1Z5kUzncCZcO02o63t8YgZ9rW402i26fD4He9pB3Xe1pM3TgFaPY9kaYNb3ck5efNCuVZaAqr6kgz2GqQdq3ZkWzZWpcuz8DNfrMcwibFnlxbHzXANcKkcvarWli2b6tVaQDvZcrvr37zIKrO0l1rV5YLOYShpiXiqU9Y0k0piIzRfrk/TAZDLp9tnUoey79WDzKh9OTy3du5P1vZyj4zH4Gm1Y7W+syfWo6ks62LPZbHqbQBbW9hJ2qxn9bZ6a7uxR1lfdtdBzZ4+DPe2g7Lv1YEuXF7m8smTvTtb3co6Oz2BLl7dm32mq+pIO9qanp/U2gSys7eVsXuXFsfFYzWrBKOtbPImrY7CXz+d1+2zqUPbdeqDueC/18Mj6XiKdzePkhTi21DBLQFVf0sEew9SLK7t8CF9MTzLLc+z8DLqaGrknFsMswmp/I7wN1mItGrM0p6fiSOfyXK9XAqSDPbud/5hoBWt7Oepi8/oyLRPKgbK+x8/HdN3VAziNqyWUfbcemEwmbF7lW3Jnj/W9hNqippbBHlV9SQd7VJsjigBrezmbOr0wm5bvj1UOVPVNpLIYDiV0P4nLTZW1g6rv1pMtXV68MTGDbG5huQHre4mj4zOFmcLNzppdk6q+pFe8UCiktwlkYW0vp9Fuwbo2T8129qjqe+z8DBQFuGq1vgtqLrf4SUemeqj6bj3ZvMqHVDaPoeDCnm+s7yWOXswSmM2126mnqi/pYI8bp2oHa7uQLV0+HK3RIQ2q+r6uQdqFEQuqvltPtiwzSYP1LZDN5XFiYgZX1ngtoaov6WCP63K0g7VdyJVdXoRm05icSVV9Lar6vj42jU5fA9o8DXqbwmgEVd+tJz0BNxptlkUPabC+BYaCCcxn8sXAuFZQ1Zd0sBcIBPQ2gSys7UKuXF27QxpU9X1tPCbErp7Fos+YNiNA1XfricVswqZV3kV39ljfAsXDGTVuzk5VX9LBXizGR9e1grVdyKZOH8ym2gR7FPWNz2cwEkrgKgGCPe6zpx0UfVcPrlrtw9HxhYc0WN8CR8/H0GAzo7fVXdPrUtWXdLCXTqf1NoEsrO1CGu0W9Le5a3Iil6K+6uGMK3U+nAHQrcsRAYq+qwdXrfZhLpNbcEiD9S1wbHwGmzq9sNTwcAZAV1/SwR7D1JstXb6ancilxutjBV1qXVDNMBS5anUTAODVMZoTHaohl1dw7LwYJSGyQDrYa2pq0tsEsrC2i3Nllw/BeAqTM9VN0qCo72vjMXQ1NaLF7dDbFO6zpyEUfVcPelpc8DisxYckFdYXGArOIpHOYevq2mtBVV/SK14mk9HbBLKwtouj7lq9dYEuF4r6Hh2PCbOrx2lc7aDou3pgNpuwpcuH196ys8f6AkfOFTTZuqb26wlVfUkHe4nEwoaUTG1gbRdn0ypvTQ5pUNM3Nlc4nCFCvR7AwZ6WUPNdPblqtQ8nJuJIZy8d0mB9gdfGpuF2WNEbqO3hDICuvqSDPYapN067FevaPAuexo3OsXGu12OYcrlqdRPSuTxOXojrbYpQvDZWyBLUcnIGdUgHey6XS28TyMLaLs3WNT4cOTdd1e4RNX1fEyzYo9o4VQSo+a6eqGMFXxu/9PBodH1T2RxOTMzgKg1SuABdfUkHe1arVW8TyMLaLs3WNU2IJjM4F5mr+BrU9H19PIbV/kb4XXa9TQHAwZ6WUPNdPVntb4TfacNr5y6VhRhd3xMTcWRyiiaHMwC6+pIO9qg2RxQB1nZptq0pLEJHqkjlUtP31XPTmi3OlcBNlbWDmu/qiclkwpWrmy5rv2J0fdUSmas0qv+lqi/pYI9h9GCg3YMGmxmvnuO6PQAIxlMYi84Vg2CGYUrnqi4fTk/NYi6d09sUIXj1XAwBtx1dTY16myIVpIM9u12MlBFFWNulsVnM2LLKV2wPUAmU9FV12LZWnGCP07jaQcl3ReCq1T7k8gqOTxR2nIyu76tj07hqdZNm32Gq+pIO9rxer94mkIW1XZ6ta5pwdDyGTK6ydCElfY+ci8JiNtV8YHk1cLCnHZR8VwS2qmUhF+v2jKzvbCqLoeCsZilcgK6+pIO9UCiktwlkYW2XZ+uaJqSylbdMoKTvkXPT2NDhQaPdorcpRbhmTzso+a4ItHsbsMrXUNwhN7K+r4/FoCiXAmAtoKov6WCPYfTibWt4riUA5PMKXjsX43o9hqmCt63145WzUb3N0B31cIZIh71kgXSwx6ka7WBtl2e1vxHNLjuOnK0s2KOi71BwFvFUloM9A0HFd0XibWubMBadw1R83tD6vjo2XVxbtYKqvqSDvUAgoLcJZGFtl8dkMmHral/FO3tU9H3lYurpbQIdzgAAi0WclDI1qPiuSKjfnyNnpw2t78tnpvG2tX5NP4OqvqSDvelpY6fQtIS1XZmta5pwemoWs6ls2e+lou+Rc9PwNCw9wzISiWDnzp1Yt24ddu7ciWh0YarqF7/4BbZt21b8v4aGBjz22GMAgI9//OPo6ekp/u7IkSMl2cU1e9pBxXdFYvMqH2wWE145N21Yfc9Pz+HCzDyu1vjBkaq+pIO9TCajtwlkYW1XZtuaJigKKpqTS0XfI2cLzZSXmmG5d+9e7NixA6dPn8aOHTuwd+/eBa+55ZZbcOTIERw5cgRPPfUUnE4nbrvttuLv//Iv/7L4+23btpVkVzWj7JjloeK7ItFgs2BTpxevnI0aVt+XL9YsXq3xzh5VfUkHewyjJ2qd2isV1u3Jzlw6h5OT8WXr9fbv34/du3cDAHbv3l3csVuKH//4x3jve98Lp9NZU1sZRnTettaP18ZiyOaN+aDy8plpOKxmbOyk2RpFa0gHe36/tk8ARoa1XZkmpx39bW785kz5p+go6Pv6eAy5vLJssDc5OYnOzk4AQEdHByYnJ5e95iOPPIKPfvSjl/3s85//PK666ircf//9SKVSJdlmNpNe+nSFgu+KyNvWNiGZziGUpjm7dSVePhvF1tVNsFu1/e5S9d+yvCaZTCKRSAAAXC4XrFZrcY6c3W6H1+st9qgxmUwIBAKYnp4ubov6/X6kUikkk0kAgNvthsViKV7D4XDA4/EUr2E2m9HS0oJoNIpstlD31NzcjLm5OczNFYbMezwemEwmzMzMAAAaGhrgcrkQDocxNzcHt9uN5uZmRCIR5HKFcTMtLS1IJBKYn58HUGiiqCgK4vFCT7TGxkY0NjYiEokURLJa4ff7EQ6Hi7U+gUAA8Xi8+MfF5/Mhl8thdnYWAOB0OuFwOIo1SDabDU1NTQiFQsUUUiAQwMzMDNLpdPEa2Wz2Mo1tNluxhsBut8Pn8xWvoWoci8WK12hqakImk9H8PpnN5uI9qPY+AYWCeYr3aXNbI54ejCCfVxCPz5R8n7LZLDo6OoT6PpV7n351YgwA8OU//UN8auJM8fXqdR588EEoioJgMFi8TwAQDAYXvU+RSASvvfYarr76akxPT6OpqQkPPPAA2trakE6n8fnPfx5f+tKXcP/99y96nx599FH84Ac/gNVqRSQSQSwWE+b7pOd9qvX3KZvNFvWjtu7peZ/Wugr34NmT57FplU/oda/W92kyFMHR8RjuvqYD2WxW0/s0NzcHv98vzPdppfvU2tqKUjCVWbsi1f5xMBgsWQimPFjb0vjnF8/hs//6Gg5++ib0ty1+SGExKOj7//zjSzgxEcczn71lydesX78eTz/9NDo7OzExMYGbb74ZJ0+eXPS13/rWt3Ds2DH83d/93aK/f/rpp/FXf/VXeOKJJ5a1y2QyYWBgYMnPYaqDgu+KiKIouPa/HsQN3V58+/e3621OXfnNmQg+9LfP4bu/fw1u39yh6WdJ6L8l9YrhXAbDaMjVVxRSAi9XkMqVGUVR8NJoFNdesXxKZNeuXdi3bx8AYN++fbjjjjuWfO2PfvSjBSnciYmJ4uc99thj2LJlS5WWM4yYmEwmvG1tE45OzOptSt15+Uxh91DrwxmUIR3sud2l76Qw5cHalkZvwIUmp63suj3Z9R0NJxFOpHFtd/Oyr9uzZw8OHDiAdevW4eDBg9izZw8A4KWXXsJ999136Xqjozh37hxuuummy95/zz334Morr8SVV16JUCiEL3zhCyXZxzV72iG774rM29b6MRqZx3QyrbcpdeXls1GsaW5Eq8eh+WdR9V/SlZ7cOFU7WNvSMJtNuHqtH78pc9SR7Pq+NFqoU7m2e/kn8ZaWFhw6dGjBz6+99lp873vfK/53d3c3xsfHF7zuqaeeqtJSptbI7rsiozZXfvlsFLduaNfZmvqgKApePhvF9b0tdfk8qv5L+vFWLdhkag9rWzrXXOHH4NRsWU/jsuv7mzNReBus6G8V8ymZmyprh+y+KzJvW+OHxWzCi6PGKQs5H5vH5Eyqbilcqv5LOthjGBG4Rq3bM9Ag85fORHFtd/OSzZQZhimfRrsFG9udxZ1zI6DWO3O9XnWQDvYcDu3z+0aFtS2draubYDGbyqrbk1nfaCKNwanZYpArIlSHnYuAzL4rA1evbcKr52KYz+RWfjEBXhqNwGm3YEOnpy6fR9V/SQd7Hk99nMOIsLal02i3YPMqb1nBnsz6qv/OlU7i6gkHe9ohs+/KwDsHOpDO5fH6OM1041t5YSSCq9f6YbPUJ1yh6gcU4nUAABX+SURBVL+kgz21qSJTe1jb8rh6rR+vnoshkyutVkxmfV86E4XNYsLWZSZn6A3X7GmHzL4rA73ugu8eHqGfyo0lMzg5Gcfbe5Y/1V9LqPov6WCPYUTh2m4/5jI5HDXA0/hvzkSwpcuHBhvNU20MoydNThv629yGqNt76UwEioK6BntUIR3scS8t7WBty0NdrF4o8WlcVn1T2RxeHYsJncJltEVW35UFs9mM67r9eOlMFPm8VEOtyubwSAQ2i2nZ+dq1hqr/0vxXXaSlpT59eYwIa1sebZ4G9La68MJwuKTXy6rv0fEY0tk8rrlC7Cdxqr20REBW35WFlpYWXNfdjPh8Ficn43qboymHRyPYurqprlkCqv5LOthThwcztYe1LZ/tPS14aTSKXAlP47Lq+/xwYefyuhWaKesN1+xph6y+KwvRaBTXXZxMQzmVm0xn8fpYDNfVOYVL1X9JB3vZbFZvE8jC2pbP9b3NiKeyOH5+ZsXXyqrv88NhrG/3oMUtdvsCRaGd/tITWX1XFrLZLFb7G9HudeAw4ebKr5ydRjav1L1ej6r/kg72GEYktvcU0gMvjJSWypWNTC6Pl0ajuL5X7BQuw8iOyWTCdd3NeHEkQvbB5fBIBGYThO7XKROkg73mZv6joxWsbfl0+BpwRYuzpEMaMur72tg05jK5us2wrAaqRdgiIKPvyoSq7/beFlyYmceZcFJni7Th8EgEGzu98DbY6vq5VP2X9Io3NzentwlkYW0rY3tPM14cjax4ik5GfdV6ve0SBHtUd0NEQEbflQlV33f0Fb5nvx6ilylIZ/N45VxUl5YrVP2Xgz2mIljbytje04Lpi41Cl0NGfZ8fDmNDhwfNLrvepqwIB3vaIaPvyoSqb2/AhXavA78eotcE+PXxacxn8nh7Nwd7tYJ0sMcworH9Yj1bqS1YZCGdVev1xN/VYxgKmEwmvKMvgOeHw+QeXp4dDMNkAq8nNYR0sEd1xp0IsLaVsdrvRFdT44p1e7Lpe6leT456F67Z0w7ZfFc23qzvDb0tCM2mcXpqVkeLas+vBkPYvMoLvw5ZAqr+S3rF42Hn2sHaVs71vS14fji8bN2ebPo+f3GnUj1xzBgX2XxXNt6s7w1q3d4gnVRuMp3FK2ejeGdfQJfPp+q/pIO9mZmV+5kxlcHaVs6N61oQTWZwbJl+e7Lp+9zFej09nsQrgZsqa4dsvisbb9Z3TbMTa5ob8RyhspAXR6PI5BS8o1+fYI+q/5IO9hhGRN55cRH7FZGn8VQ2h9+c4Xo9htGDd/QG8PxwpKTJPDLw68EQbBaT8FN4ZIN0sNfQ0KC3CWRhbSunzdOADR0e/GowuORrZNL3N2eimM/ki60gZIBqqkYEZPJdGXmrvjf0tSA2l8GJCRo7Ur8aDOHqtX447VZdPp+q/5IO9lwul94mkIW1rY4b+wN4cTSK+Uxu0d/LpO8zp0Kwmk3F+iEZ4GBPO2TyXRl5q77q9+45Av32ook0jk/MFLMfekDVf0kHe+Gw/M4vKqxtddy4LoB0No/DS5zKlUnfX54O4uor/PDUudN9NXDNnnbI5Lsy8lZ9270N6Gt1kSgLeW44DEUB3tmv34MjVf8lHewxjKi8vacZdotZ+gU6GE/h2PkZ3DTQqrcpDGNY3jXQiueHw0tmCmTh2cEQ3A4rrlrdpLcp5CAd7FksFr1NIAtrWx1OuxVXX9GEX55ePNiTRV+17vBd6zjYYwrI4ruyspi+Nw20IpXNlzR3W2R+PRTG9p5m2Cz6hSZU/Zd0sEd1oLEIsLbV81vrWnFiYgbBeGrB72TR95enQmh22bF5lVdvU8qC6oIuArL4rqwspu/1vS1wWM14+uSUDhbVhjPhBEZCCdy4Tr96PYCu/5IO9iIRuZ9yRIa1rZ4bLxYhLzbbUgZ983kFz5wO4cb+AMxmuQ485HJyp7tERgbflZnF9G2wWXB9bwv+96mlT/iLzi/eKASqt6xv09UOqv5LOtjjBV07WNvq2dLlQ5PThmdOLQz2ZND3jQtxhGZTeBfX6zFvQgbflZml9L15fSuGgwmciyTrbFFt+MXJIHoDLnQH9D0NS9V/SQd7DCMyFrMJNw204umTU1I2RH3mdGEX4bd0TrswDIPiISkZU7nJdBbPDYdxs867epQhHey1tMjT90s2WNvacOuGNoQTabw6Nn3Zz2XQ95lTQWzo8KDdK18TUrOZ9NKnKzL4rswspW9PwIW1zU4pU7nPDYWRzuZx6wb9gz2q/kt6xUskEnqbQBbWtjbcPNAGi9mEQycmL/u56PrOzGdweCSCm9bLmcJVFPl2UmVBdN+VnaX0NZkKmYJfD4WRysqVinzqjSk47RZc16P/iDSq/ks62Jufn9fbBLKwtrXB57Th2iv8OHTi8tSL6Po+fTKIbF7BbZva9TalIjjY0w7RfVd2ltP3poFWJNM5vDQaraNF1aEoCp4+GcSN/QE4rPqfkqfqv6SDPYaRgXdvbMcbF+IYi8pTWH3g+CQCbju2rdH/SZxhmALv6G+B3WrGwbdkCkTm1OQsxqfncIsAKVzKkA72vF65en/JBGtbO27dWFjk1NYDgNj6prN5PP3GFHZsaIdFspYrKlyzpx0i+y4FltPXabfit/oD+PmxSWl2r39xUoyWKypU/Zf0iieLs8sIa1s7+lrd6Am4cPBNqVyR9T08EkE8lcW7JU3hMtoisu9SYCV9b9/cgfHpORw7P1Mni6rjqRNT2NjpRYdPjINeVP2XdLAXj8f1NoEsrG1tuXVDG54bCiORygIQW98Dxy+gwWYuNoWWkXw+r7cJZBHZdymwkr47NrbBbAJ+fuxCnSyqnKn4PF48E8Htm8V5cKTqv6SDPYaRhR0b2pDO5ZeclSsKiqLgwPFJ3Njfika7/sXUDMNcTovbgeu6m/GzY+LX7f3s2CQUBXjflZ16m0Ie0sFeY2Oj3iaQhbWtLdf1NMPvtOGnRycAiKvv8YkZnI/NS3sKV8VkkrPWUAZE9V0qlKLv7Zs7cHIyjtGQ2G1Enjw6gd5WF9a1ufU2pQhV/+Vgj6kI1ra22CxmvGdLBw4en8R8JiesvgeOT8JkunSoRFY42NMOUX2XCqXoe9vFtOjPBE7lhmdTeH44gvdt6RTq+0jVf0kHe1QHGosAa1t7fvvKVUikc3j65JSQ+iqKgidem8B13c0IuB16m1MVXLOnHSL6LiVK0Xe134ktXV6hg70DxyeRyyt475UdeptyGVT9l3SwxzAycX1vM/5Pe/ceFNV1xwH8e5eFxeWxPBZYHosPFnERRMVIjNG8pGoSXwkJJNUYMzYdYzIxtqmddqY1Eyexk85YbZwYM0STNGqi0WAlmFRRDNEIKD4SUOQpLAiyCssbdvf2D4klDSqPPXvvHn+fGf9gXe79zW8Ov/nde849N9DLAwfO1UkdSr+K61pQ2tCK+QlhUodCCLmD2bE6nL7chHqLPDcJzvrhCiID1IgN5XOrE7nhutlTKpVSh8Atyq3jKXuncg8XN6BHlM+0xk/2n62FUiFwsZhaTtNGvKHawNZA8zu39+9UjhePze09+K60EXPjdbL7W+R1/HLd7Pn70+7+rFBu2XhsQig6emwovNIjdSg/Y7eL+PfZWsyI1iLAy0PqcIaNNlVmh2oDWwPNryHYG/HhGnxZaGIc0eAdKq6H1S5ibpz8Lhx5Hb9cVzyz2Sx1CNyi3LKRNDoQWm8V9hZUSh3Kz5y+fB2mpg7Mn8jHFK7N5lovinclVBvYGkx+F00Kx3lTM0ob5LV33JdnTIjwH4GECI3UofwCr+OX62aPFmGzQ7llw00h4NF4HXLLm25usCwH+8/WQqVUIDlWXoupifxQbWBrMPmdlxAGN4WAfTK6u1fb1IHc0kY8OTlCdlO4AL/jl+tmjxBXND8hDF1WOzLPy2OtjdVmR+a5OsyKDYG3is/1LITwKMhHhfsNWnxZWAu7XR6vAdtXaIIoAk9OjpA6lLsK182eVuu6r3OSO8otO4kj/REV5IXP8qulDgUAkFvaCHNbN1dP4dKaPXaoNrA12Pw+MTkcpqYO5FdKv6WIKIrYc6oGSaMDEBmoljqcfvE6frmueLy+404OKLfsCIKABfHBOFV1XRZrbT7Lr0aAlwcejAmSOhSH4fVl53JAtYGtweY3OTYEag83WUzlnr58HRWNbUhJlO9dPV7HL9fNXldXl9QhcItyy9bsGD8oFYLkd/caLJ34pqgeTyVGQKXk51241OyxQ7WBrcHmV+2hxJzxOmSer0NHt7QPJu05VQO1h5ust2/idfxy3ewR4qoC1O6YZQzB3tMmdFulWzD8eUE1bHYRz0yNlCwGQsjwpE2NREunFRlnpLu719Ftw4GzdZgTp4MXrf11Oq6bPY1Gfo9184Jyy5ZGo0HqVD3Mbd04XFwvSQw2u4idedW436DFKK2XJDGwQmv22KHawNZQ8nvPKH+M0/ngoxNVkt3Vzjxfh5Yuq6yncAF+xy/XFY/20mKHcsuWzWbDzOgghGo8sUuiqdyckgaYmjrwbBLd1SMDR7WBraHkVxAELL1vFIrrLCious4gqtsTRRHpuRUYG+KNaWMCnX7+weB1/HLd7LW2tkodArcot2y1trbCTSEg9R49ckquorTB+fnecfIygnxUSI4Ncfq5WeN1Ly05oNrA1lDzu2BiGHw9lfjoeKVjAxqAE+VmFNdZ8ML00bLcW68vXscv180eIa5uyb0j4emuwAfHyp163upr7ci+0IDUKXq4u1GZIMTVqT2UeHqKHgd/uIJ6S6dTz/1hbiUCvDywcFK4U89L/ofrKq5Wy3MfHx5Qbtn6Kb+B3io8PUWPfYUmpxboLTllUCoUWHzvSKed05nkfnfBlVFtYGs4+V1870jYRBGfnrzswIhur7KxDYcv1GNxUiQ83eX/RD+v45frZk+lUkkdArcot2z1ze/y+8fAardj23eVTjn3leZO7C6oQcqUCOg0nk45p7NRs8cO1Qa2hpPfUVovPBwTjE9OVKLVSa9j3PZdBZQKAYunucaFI6/jl+tm7/p15y9EvVtQbtnqm9/IQDXmxofi0++r0NLZw/zcW4+VwyaKWPFAFPNzSYXW7LFDtYGt4eb3lUeicb29xylr9661dWP3qRrMSwhDsI9rXDjyOn65bvYI4cVvZ45BS5eV+fRLY2sXduRVYeHEcOgD+JzOIORuNlHvh1nGYGw9Vg4L44vHd7NL0dljw0sP8nvh6Cq4bvbc3d2lDoFblFu2/j+/EyL8MCNaiy05ZWhq72Z23vTcCnRZ7XjpIb6LM03jskO1gS1H5HfVrLFo7ujBh7kVDoiof9XX2vGv76vwVKIehmAfZudxNF7HL9fNnp+fn9QhcItyy1Z/+f3To0ZYOnqw8fAlJuesa+7AR8cr8Vh8KKKCvJmcQy5oU2V2qDaw5Yj8xoVrMGe8DunfVqC5nc3dvQ3/KYEgAKuSo5kcnxVexy/XFa+xsVHqELhFuWWrv/waQ32RNjUSn5yoYrLv3roDxbDZRayZM87hx5YbXjdOlQOqDWw5Kr+rkqPR0mXFu0ccf/FYVGvBvjMmLJs+GqGaEQ4/Pku8jl+umz162Tk7lFu2bpXf1cljMcLdDW99VezQ8x0ruYrM83VY+ZCB1uqRYaHawJaj8jtO54tnpuqRnluB8zXNDjkmcCO+t7OK4aNSuuRDXryOX66bPUJ4o/VW4eWHDci+0IAjFxoccswuqw1/3f8jRgWq8eLMMQ45JiFE/v441wittwp/+OIcemyOeUJ9R95lfHupEb/7VQw0aj7Xv7kirps9rVYrdQjcotyydbv8Pj99FKKDvfH6nrNocMBGy+8dLUNFYxveWBDnEpueOgKt2WOHagNbjsyvZoQ73lwYh+I6C7Y64C09FY1tWHegGDOitVjiohuy8zp+ua54FotF6hC4Rbll63b5VSndsPnXk9HaZcWru87AZh/6tMPRiw3YePgSFk4MwwNjg4Z8HFfD61SNHFBtYMvR+Z09XodH43XYeOgSimqHfmyrzY7Vn5+Bu5uAd1ISoFC45hPvvI5frpu97m52W1Tc7Si3bN0pv2NDfPDmgjicKDdj0xCfzi2/2opXdhZinM4Xbz0RP6RjuCpq9tih2sAWi/y+MT8O/l7ueGF7PmqbOoZ0jL9/U4LCy01Ytyjepd+8w+v45brZI4RnT03R44nJ4diUfQm7C6oH9buWzh785uMCuLspsHVJItQeSkZREkLkLshHhe3LpqKty4rnt+UNejuWjYcuYUtOGZ5NisT8hDBGUZLh4LrZ02g0UofALcotWwPN77qFcZgepcXre87h/ZyyAf1OlbkNKe8dR5W5HZufnSzp07e7d+/G+PHjoVAoUFBQcMvvHTx4EDExMTAYDFi/fv3NzysqKpCUlASDwYDU1NQBX5XTmj12qDawxSq/xlBfvP9cIioa27D843xcaxvY39LmI6XYcKgEKYkRWLcgjklszsTr+OW64lmtznnR892IcsvWQPOr9lAi/fkpeHxCKN7OuoC1+3+87SuQckquYt4/c9HQ0oXty6ZiWlSgo0Iekri4OOzduxczZ8685XdsNhtWrlyJrKwsFBUVYefOnSgqKgIArFmzBq+99hpKS0vh7++P9PT0AZ2XpnHZodrAFsv83helxYbUiThb3YzZ/ziGIxdv/cR/vaUTq3YV4p2vL2LRpHD87ckJLrtOry9exy/XczdtbW1Qq2nPMBYot2wNJr8qpRs2pU2C1luF7ccr8cXpGiy7bxQeTwiDr6c7PJQKHL3YgD2nanC8zIxxOh988NwUWeynZzQa7/idvLw8GAwGjBlzY1uYtLQ0ZGRkwGg0Ijs7Gzt27AAALF26FGvXrsWKFSvueExq9tih2sAW6/w+PiEMUUHeWLXrDJZty8djE0KRbAzBdIMWbgoBpQ2tOFluxpacMvTYRLzysAGvPhINNw4aPYDf8ct1s0fI3UKhELB2/nikJEZg85FSbMq+8a+vyAA1ViePxfIZo11qjZ7JZIJer7/5c0REBE6ePAmz2Qw/Pz8olcqbn5tMpgEdk6ZxCbk1Y6gvMl6ejg2HSvBZfjUyz9X94juPjAvGX+bFYmSglwQRksES6AqXEMKSIAiHAOj6+a8/i6KY0fudowB+L4riLxbuCYKQAmCOKIrLe39eAiAJwFoA34uiaOj9XA8gSxTFfhcOCYLwIoAXe3/0vNX3CCGEN65zeU8IcUmiKM4a5iFMAPR9fo7o/cwMwE8QBKUoitY+n98qjq0Atg4zFkIIcTk0l0EIkbt8ANGCIIwWBMEDQBqA/eKNaYkjAFJ6v7cUQIZEMRJCiGxRs0cIkYwgCIsEQagBMA1ApiAIX/d+HiYIwlcA0HvX7mUAXwMoBvC5KIo/9h5iDYDVgiCUAggEMLDHcQkh5C5Ca/YIIYQQQjhGd/YIIYQQQjhGzR4hhBBCCMeo2SOEEEII4Rg1e4QQQgghHKNmjxBCCCGEY9TsEUIIIYRwjJo9QgghhBCOUbNHCCGEEMKx/wLpreAHR8MlsgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 792x792 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "x=np.linspace(-2*np.pi,2*np.pi,200)\n",
    "y=np.sin(x)\n",
    "fig3,axes3=plt.subplots()\n",
    "fig3.set_size_inches(11,11)\n",
    "axes3.set_xlim(-7,7)\n",
    "axes3.set_ylim(-1,1)\n",
    "axes3.plot(x,y)\n",
    "axes3.spines['top'].set_visible(False)\n",
    "axes3.spines['right'].set_visible(False)\n",
    "axes3.spines['left'].set_position(('data',0))\n",
    "axes3.spines['bottom'].set_position(('data',0))\n",
    "## 为图像添加网格\n",
    "axes3.grid(color='gray',linestyle='--',alpha=0.2)\n",
    "# 为X轴，Y轴绘制箭头\n",
    "# 绘制 x轴箭头\n",
    "axes3.arrow(-7,0,13.8,0,width=0,shape='full',head_width=0.05,head_length=0.2)\n",
    "# 绘制 Y轴箭头\n",
    "axes3.arrow(0,-1,0,1.97,width=0,shape='full',head_width=0.25,head_length=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-21T03:42:30.250440Z",
     "iopub.status.busy": "2022-03-21T03:42:30.249483Z",
     "iopub.status.idle": "2022-03-21T03:42:30.383004Z",
     "shell.execute_reply": "2022-03-21T03:42:30.381945Z",
     "shell.execute_reply.started": "2022-03-21T03:42:30.250393Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "# 我们使用numpy中的数组来表示向量\n",
    "v=np.array([2,1])\n",
    "# 绘制图像，展现响亮的空间表达\n",
    "fig,axes=plt.subplots()\n",
    "axes.set_xlim(0,3)\n",
    "axes.set_ylim(0,3)\n",
    "axes.grid()\n",
    "axes.arrow(0,0,v[0],v[1],head_length=0.1,head_width=0.1,color='blue')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-21T03:53:18.722451Z",
     "iopub.status.busy": "2022-03-21T03:53:18.722043Z",
     "iopub.status.idle": "2022-03-21T03:53:18.946822Z",
     "shell.execute_reply": "2022-03-21T03:53:18.946037Z",
     "shell.execute_reply.started": "2022-03-21T03:53:18.722418Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/cbook/__init__.py:2064: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
      "  x[:, None]\n",
      "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/axes/_base.py:248: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
      "  x = x[:, np.newaxis]\n",
      "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/axes/_base.py:250: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
      "  y = y[:, np.newaxis]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(1950,5000,'1979年改革开放')"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "fig,axes=plt.subplots()\n",
    "axes.plot(year,china)\n",
    "axes.annotate('1979年改革开放',xy=(1979,1064),xytext=(1950,5000),arrowprops=dict(facecolor='r',shrink=0.05),fontproperties=myfont)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-21T05:33:46.904417Z",
     "iopub.status.busy": "2022-03-21T05:33:46.903499Z",
     "iopub.status.idle": "2022-03-21T05:33:47.698495Z",
     "shell.execute_reply": "2022-03-21T05:33:47.697655Z",
     "shell.execute_reply.started": "2022-03-21T05:33:46.904384Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.font_manager import FontProperties\n",
    "## 中文显示问题\n",
    "myfont=FontProperties(fname=r\"/home/aistudio/external-libraries/微软雅黑.ttf\",size=12)\n",
    "#创建figure对象以及对象包含的多个子图\n",
    "fig2,axes2=plt.subplots(2,2)\n",
    "fig2.set_size_inches(12,12,)\n",
    "fig2.suptitle(\"各国GDP\",fontproperties=myfont)\n",
    "ax1=axes2[0,0]\n",
    "ax1.set_title(\"中国\",fontproperties=myfont)# 给第一个图赋值titile\n",
    "# 我们单独给第一个图的X轴Y轴设置了不同的数值范围\n",
    "ax1.set_xlim([1800,2040])\n",
    "ax1.set_ylim([0,80000])\n",
    "ax1.plot(year,china,color=\"red\",label=\"GDP\")\n",
    "# 定义第三个图\n",
    "ax2=axes2[1,0]\n",
    "ax2.set_title(\"美国\",fontproperties=myfont)\n",
    "ax2.set_xlim([1800,2040])\n",
    "ax2.set_ylim([0,80000])\n",
    "ax2.plot(year,usa,color='blue')\n",
    "# 加入图例\n",
    "ax1.legend()\n",
    "ax2.legend(('the gdp of usa',),loc='right',fontsize=12)\n",
    "fig2.subplots_adjust(left=None,hspace=0.5,wspace=0.3)\n",
    "# 调用现有风格\n",
    "plt.style.use('ggplot')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-21T05:33:55.942329Z",
     "iopub.status.busy": "2022-03-21T05:33:55.941318Z",
     "iopub.status.idle": "2022-03-21T05:33:56.805608Z",
     "shell.execute_reply": "2022-03-21T05:33:56.804907Z",
     "shell.execute_reply.started": "2022-03-21T05:33:55.942278Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.font_manager import FontProperties\n",
    "import os\n",
    "## 中文显示问题\n",
    "myfont=FontProperties(fname=r\"/home/aistudio/external-libraries/微软雅黑.ttf\",size=12)\n",
    "#创建figure对象以及对象包含的多个子图\n",
    "fig2,axes2=plt.subplots(2,2)\n",
    "fig2.set_size_inches(12,12,)\n",
    "fig2.suptitle(\"各国GDP\",fontproperties=myfont)\n",
    "ax1=axes2[0,0]\n",
    "ax1.set_title(\"中国\",fontproperties=myfont)# 给第一个图赋值titile\n",
    "# 我们单独给第一个图的X轴Y轴设置了不同的数值范围\n",
    "ax1.set_xlim([1800,2040])\n",
    "ax1.set_ylim([0,80000])\n",
    "ax1.plot(year,china,color=\"red\",label=\"GDP\")\n",
    "# 定义第三个图\n",
    "ax2=axes2[1,0]\n",
    "ax2.set_title(\"美国\",fontproperties=myfont)\n",
    "ax2.set_xlim([1800,2040])\n",
    "ax2.set_ylim([0,80000])\n",
    "ax2.plot(year,usa,color='blue')\n",
    "# 加入图例\n",
    "ax1.legend()\n",
    "ax2.legend(('the gdp of usa',),loc='right',fontsize=12)\n",
    "fig2.subplots_adjust(left=None,hspace=0.5,wspace=0.3)\n",
    "# 调用现有风格\n",
    "plt.style.use('ggplot')\n",
    "# 导出文件到本地保存\n",
    "gfile='data/gdp.jpeg'\n",
    "if os.path.exists(gfile):\n",
    "    os.remove(gfile)\n",
    "else:\n",
    "        fig2.savefig(gfile)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-28T02:25:02.975003Z",
     "iopub.status.busy": "2022-03-28T02:25:02.974450Z",
     "iopub.status.idle": "2022-03-28T02:25:02.984670Z",
     "shell.execute_reply": "2022-03-28T02:25:02.984039Z",
     "shell.execute_reply.started": "2022-03-28T02:25:02.974968Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2-2全1数组： \n",
      " [[1 1]\n",
      " [1 1]]\n",
      "3-3全0数组： \n",
      " [[0 0 0 0 0]\n",
      " [0 0 0 0 0]\n",
      " [0 0 0 0 0]]\n",
      "指定数值3-3数组： \n",
      " [[3.2 3.2 3.2 3.2 3.2]\n",
      " [3.2 3.2 3.2 3.2 3.2]\n",
      " [3.2 3.2 3.2 3.2 3.2]]\n",
      "5-5特征矩阵 \n",
      " [[1. 0. 0. 0. 0.]\n",
      " [0. 1. 0. 0. 0.]\n",
      " [0. 0. 1. 0. 0.]\n",
      " [0. 0. 0. 1. 0.]\n",
      " [0. 0. 0. 0. 1.]]\n",
      "生成3-3空数组, \n",
      " [[6.93925384e-310 4.68619236e-310 6.93919461e-310 6.93919459e-310\n",
      "  6.93919459e-310 6.93919459e-310 6.93919459e-310]\n",
      " [6.93919459e-310 6.93919457e-310 6.93919457e-310 6.93919457e-310\n",
      "  6.93919457e-310 6.93919457e-310 6.93919457e-310]\n",
      " [6.93919457e-310 6.93919457e-310 6.93919457e-310 6.93919457e-310\n",
      "  6.93919457e-310 6.93919457e-310 8.69555537e-322]]\n"
     ]
    }
   ],
   "source": [
    "#生成数组\n",
    "import numpy as np \n",
    "ar1=np.ones((2,2),dtype=int)\n",
    "print(\"2-2全1数组：\",'\\n',ar1)\n",
    "ar2=np.zeros((3,5),dtype=int)\n",
    "print(\"3-3全0数组：\",'\\n',ar2)\n",
    "ar3=np.full((3,5),3.2)\n",
    "print(\"指定数值3-3数组：\",'\\n',ar3)\n",
    "ar4=np.eye(5,dtype=float)\n",
    "print(\"5-5特征矩阵\",'\\n',ar4)\n",
    "ar5=np.empty((3,7))\n",
    "print(\"生成3-3空数组,\",'\\n',ar5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-28T02:28:40.611877Z",
     "iopub.status.busy": "2022-03-28T02:28:40.610884Z",
     "iopub.status.idle": "2022-03-28T02:28:40.617383Z",
     "shell.execute_reply": "2022-03-28T02:28:40.616529Z",
     "shell.execute_reply.started": "2022-03-28T02:28:40.611838Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "指定数值3-5数组： \n",
      " [[3.2 3.2 3.2 3.2 3.2]\n",
      " [3.2 3.2 3.2 3.2 3.2]\n",
      " [3.2 3.2 3.2 3.2 3.2]]\n"
     ]
    }
   ],
   "source": [
    "print(\"指定数值3-5数组：\",'\\n',ar3)\n",
    "ar3=np.full((3,5),3.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-28T02:26:30.069155Z",
     "iopub.status.busy": "2022-03-28T02:26:30.068503Z",
     "iopub.status.idle": "2022-03-28T02:26:30.074283Z",
     "shell.execute_reply": "2022-03-28T02:26:30.073599Z",
     "shell.execute_reply.started": "2022-03-28T02:26:30.069119Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2-2全1数组： \n",
      " [[1 1 1 1 1]\n",
      " [1 1 1 1 1]\n",
      " [1 1 1 1 1]]\n"
     ]
    }
   ],
   "source": [
    "#生成数组\n",
    "import numpy as np \n",
    "ar1=np.ones((3,5),dtype=int)\n",
    "print(\"2-2全1数组：\",'\\n',ar1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-28T02:29:55.483041Z",
     "iopub.status.busy": "2022-03-28T02:29:55.482177Z",
     "iopub.status.idle": "2022-03-28T02:29:55.588750Z",
     "shell.execute_reply": "2022-03-28T02:29:55.587823Z",
     "shell.execute_reply.started": "2022-03-28T02:29:55.483003Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24\n",
      " 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48\n",
      " 49]\n",
      "[ 1.  2.  3.  4.  5.  6.  7.  8.  9. 10. 11. 12. 13. 14. 15. 16. 17. 18.\n",
      " 19. 20. 21. 22. 23. 24. 25. 26. 27. 28. 29. 30. 31. 32. 33. 34. 35. 36.\n",
      " 37. 38. 39. 40. 41. 42. 43. 44. 45. 46. 47. 48. 49. 50.]\n",
      "[            1             2             4             8            16\n",
      "            32            64           128           256           512\n",
      "          1024          2048          4096          8192         16384\n",
      "         32768         65536        131072        262144        524288\n",
      "       1048576       2097152       4194304       8388608      16777216\n",
      "      33554432      67108864     134217728     268435456     536870912\n",
      "    1073741824    2147483648    4294967296    8589934592   17179869184\n",
      "   34359738368   68719476736  137438953472  274877906944  549755813888\n",
      " 1099511627776 2199023255552 4398046511104 8796093022208]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#从数值范围创建数组\n",
    "import numpy as np \n",
    "a1=np.arange(1,50,1)# 注意arange是左闭右开区间[start,end)\n",
    "print(a1)\n",
    "a2=np.linspace(1,50,50)#注意linspace是左闭右闭区间[start,end]\n",
    "print(a2)\n",
    "a3=np.logspace(0,43,44,base=2,dtype='uint64')#注意需要用uint64数据类型，否则会溢出\n",
    "print(a3)\n",
    "import matplotlib.pyplot as plt \n",
    "fig,axes=plt.subplots()\n",
    " \n",
    "axes.plot(a3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-04T02:05:17.510012Z",
     "iopub.status.busy": "2022-04-04T02:05:17.509582Z",
     "iopub.status.idle": "2022-04-04T02:05:17.521782Z",
     "shell.execute_reply": "2022-04-04T02:05:17.521039Z",
     "shell.execute_reply.started": "2022-04-04T02:05:17.509980Z"
    },
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 1  2  3  4  5  6  7  8  9 10 11 12 13 14 15]\n",
      "(15,)\n",
      "1\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[ 1,  2,  3],\n",
       "       [ 4,  5,  6],\n",
       "       [ 7,  8,  9],\n",
       "       [10, 11, 12],\n",
       "       [13, 14, 15]])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np \n",
    "ar=np.arange(1,16,1)\n",
    "print(ar)\n",
    "print(ar.shape)\n",
    "print(ar.ndim)\n",
    "ar.reshape(5,3)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "py35-paddle1.2.0"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
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}
